Position estimation method, position estimation device, automated guided vehicle, and sewing device

Abstract

A position estimation method according to one aspect of the present invention comprises: a learning step for acquiring, on the basis of input sensor signals, training data required to estimate the rotational position of a rotor; and a position estimation step for estimating the rotational position of the rotor on the basis of the input sensor signals and the training data. By performing the learning step, data indicating the correspondence between a segment number associated with a segment included in each of a plurality of quadrants and a pole pair number representing a pole pair position is acquired as training data. By performing the position estimation step, the initial position of the rotor is determined on the basis of the input sensor signals and the training data.

Description

[Technical Field]

[0001] The present invention relates to a position estimation method, a position estimation device, an automated guided vehicle, and a sewing device. [Background technology]

[0002] Conventionally, motors capable of accurately controlling rotor position are known to be configured with absolute angular position sensors such as optical encoders and resolvers. However, absolute angular position sensors are large and expensive. Therefore, Patent Document 1 discloses a method for estimating the rotational position of a motor rotor without using an absolute angular position sensor. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6233532 Summary of the Invention [Problem to be solved by the invention]

[0004] The position estimation method described in Patent Document 1 sometimes failed to estimate the initial rotational position of the rotor when the rotor angle was less than one rotation, making it difficult to apply the method to applications that do not allow preparatory operations to rotate the rotor to estimate the initial position, such as drive motors for robots, automated guided vehicles, sewing machines, etc. [Means for solving the problem]

[0005] One aspect of the position estimation method of the present invention is a method for estimating the rotational position of a motor having a rotor with P (P is an integer greater than or equal to 2) magnetic pole pairs, comprising a learning step of acquiring learning data necessary for estimating the rotational position, and a position estimation step of estimating the rotational position of the rotor based on the learning data. The learning step includes a first step of rotating a magnet having one magnetic pole pair and sharing a rotation axis with the rotor together with the rotor; a second step of acquiring N1 digital signals whose levels are inverted every time the magnet rotates 180° and whose phases are different from each other, using N1 (N1 is an integer of 3 or more) first magnetic sensors that face the magnet and are arranged along the direction of rotation of the magnet; a third step of acquiring N2 analog signals whose electric signals vary according to magnetic field strength and whose phases are different from each other, using N2 (N2 is an integer of 3 or more) second magnetic sensors that face the rotor and are arranged along the direction of rotation of the rotor; and a third step of acquiring N1 analog signals whose electric signals are inverted according to magnetic field strength and whose phases are different from each other, using N2 (N2 is an integer of 3 or more) second magnetic sensors that face the rotor and are arranged along the direction of rotation of the rotor. a fourth step of dividing the learning period into a plurality of quadrants each having a different N1-bit digital value based on the digital signal; a fifth step of dividing the learning period into P pole pair regions each associated with a pole pair number representing a pole pair position of the P magnetic pole pairs based on the N2 analog signals obtained during the learning period, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotation position with each of the plurality of sections; and a sixth step of acquiring, as the learning data, data indicating a correspondence between the segment numbers associated with the sections included in each of the plurality of quadrants and the pole pair numbers representing the pole pair positions. The position estimation step includes a seventh step of acquiring the N1 digital signals using the N1 first magnetic sensors; an eighth step of acquiring the N2 analog signals using the N2 second magnetic sensors; a ninth step of identifying a current quadrant from among the multiple quadrants based on the N1 digital signals acquired in the seventh step; a tenth step of identifying a current section from among the multiple sections based on the N2 analog signals acquired in the eighth step; and an eleventh step of determining, as an initial position of the rotor, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant based on the learning data.

[0006] Another aspect of the position estimation method of the present invention is a method for estimating the rotational position of a motor having a rotor with P (P is an integer greater than or equal to 2) magnetic pole pairs, comprising a learning step of acquiring learning data necessary for estimating the rotational position, and a position estimation step of estimating the rotational position of the rotor based on the learning data. The learning step includes a first step of rotating a magnet having one magnetic pole pair and sharing a rotation axis with the rotor together with the rotor; a second step of acquiring N3 analog signals whose electric signals vary according to magnetic field strength and have a third phase difference from each other using N3 (N3 is an integer of 2 or more) third magnetic sensors that face the magnet and are arranged along the rotation direction of the magnet; a third step of acquiring N2 analog signals whose electric signals vary according to magnetic field strength and have a second phase difference from each other using N2 (N2 is an integer of 3 or more) second magnetic sensors that face the rotor and are arranged along the rotation direction of the rotor; a fourth step of calculating time series data of the mechanical angle during a learning period based on the N3 analog signals obtained during the learning period, which corresponds to one cycle; a fifth step of dividing the learning period into P pole pair regions linked to pole pair numbers indicating pole pair positions of the P magnetic pole pairs based on the N2 analog signals obtained during the learning period, further dividing each of the P pole pair regions into a plurality of sections, and linking each of the plurality of sections to a segment number indicating the rotation position; and a sixth step of acquiring data indicating a correspondence between the time series data of the mechanical angle and the pole pair numbers as the learning data. The position estimation step includes a seventh step of acquiring the N3 analog signals using the N3 third magnetic sensors; an eighth step of calculating a current value of the mechanical angle based on the N3 analog signals acquired in the seventh step; and a ninth step of determining, as an initial position of the rotor, a pole pair number corresponding to the current value of the mechanical angle based on the learning data.

[0007] Another aspect of the position estimation method of the present invention is a method for estimating the rotational position of a motor having a rotor with P (P is an integer greater than or equal to 2) magnetic pole pairs, comprising a learning step of acquiring learning data necessary for estimating the rotational position, and a position estimation step of estimating the rotational position of the rotor based on the learning data. The learning step includes a first step of rotating a magnet having one magnetic pole pair and sharing a rotation axis with the rotor together with the rotor; a second step of acquiring N4 analog signals whose electrical signals vary according to magnetic field strength and have a fourth phase difference from each other using N4 (N4 is an integer of 3 or more) fourth magnetic sensors that face the magnet and are arranged along the rotation direction of the magnet; a third step of acquiring N2 analog signals whose electrical signals vary according to magnetic field strength and have a second phase difference from each other using N2 (N2 is an integer of 3 or more) second magnetic sensors that face the rotor and are arranged along the rotation direction of the rotor; and a learning step of acquiring N2 analog signals whose electrical signals vary according to magnetic field strength and have a second phase difference from each other during a learning period corresponding to one period in mechanical angle. a fourth step of dividing the learning period into a plurality of quadrants based on the N4 analog signals obtained during the learning period; a fifth step of dividing the learning period into P pole pair regions linked to pole pair numbers representing pole pair positions of the P magnetic pole pairs based on the N2 analog signals obtained during the learning period, further dividing each of the P pole pair regions into a plurality of sections, and linking each of the plurality of sections with a segment number representing the rotation position; and a sixth step of acquiring, as the learning data, data indicating a correspondence between the segment numbers linked to the sections included in each of the plurality of quadrants and the pole pair numbers representing the pole pair positions. The position estimation step includes: The method includes a seventh step of acquiring the N4 analog signals using the N4 fourth magnetic sensors; an eighth step of acquiring the N2 analog signals using the N2 second magnetic sensors; a ninth step of identifying a current quadrant from among the multiple quadrants based on the N4 analog signals acquired in the seventh step; a tenth step of identifying a current section from among the multiple sections based on the N2 analog signals acquired in the eighth step; and an eleventh step of determining, as an initial position of the rotor, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant based on the learning data.

[0008] One aspect of the position estimation device of the present invention is a device for estimating the rotational position of a motor having a rotor with P (P is an integer greater than or equal to 2) magnetic pole pairs, comprising a magnet having one magnetic pole pair and sharing a rotation axis with the rotor, N1 (N1 is an integer greater than or equal to 3) first magnetic sensors facing the magnet and arranged along the rotation direction of the magnet, N2 (N2 is an integer greater than or equal to 3) second magnetic sensors facing the rotor and arranged along the rotation direction of the rotor, and a signal processing device that processes output signals from the first magnetic sensors and the second magnetic sensors. The signal processing device has a processing unit that executes a learning process to acquire learning data necessary for estimating the rotational position and a position estimation process to estimate the rotational position of the rotor based on the learning data, and a memory unit that stores the learning data. The processing unit performs the learning process by performing a first process of rotating the magnet together with the rotor, a second process of acquiring, via the N1 first magnetic sensors, N1 digital signals whose levels are inverted every time the magnet rotates by 180° and which have a first phase difference from one another, and a third process of acquiring, via the N2 second magnetic sensors, N2 analog signals whose electric signals fluctuate according to magnetic field strength and which have a second phase difference from one another, and a fourth process of dividing the learning period into a plurality of quadrants each having a different N1-bit digital value based on the N1 digital signals acquired during a learning period corresponding to one period in mechanical angle. a fourth process of dividing the learning period into P pole pair regions linked to pole pair numbers representing pole pair positions of the P magnetic pole pairs based on the N2 analog signals obtained during the learning period, and further dividing each of the P pole pair regions into a plurality of sections and linking a segment number representing the rotation position to each of the plurality of sections; and a sixth process of storing data indicating a correspondence between the segment numbers linked to the sections included in each of the plurality of quadrants and the pole pair numbers representing the pole pair positions in the storage unit as the learning data. As the position estimation process, the processing unit executes a seventh process of acquiring the N1 digital signals via the N1 first magnetic sensors, an eighth process of acquiring the N2 analog signals via the N2 second magnetic sensors, a ninth process of identifying a current quadrant from among the multiple quadrants based on the N1 digital signals acquired in the seventh process, a tenth process of identifying a current section from among the multiple sections based on the N2 analog signals acquired in the eighth process, and an eleventh process of determining, as the initial position of the rotor, a pole pair number corresponding to a segment number linked to the current section included in the current quadrant based on the learning data.

[0009] Another aspect of the position estimation device of the present invention is a device for estimating the rotational position of a motor having a rotor with P (P is an integer greater than or equal to 2) magnetic pole pairs, comprising a magnet having one magnetic pole pair and sharing a rotation axis with the rotor, N3 (N3 is an integer greater than or equal to 2) third magnetic sensors facing the magnet and arranged along the rotation direction of the magnet, N2 (N2 is an integer greater than or equal to 3) second magnetic sensors facing the rotor and arranged along the rotation direction of the rotor, and a signal processing device that processes output signals from the second magnetic sensors and the third magnetic sensors. The signal processing device has a processing unit that executes a learning process to acquire learning data necessary for estimating the rotational position and a position estimation process to estimate the rotational position of the rotor based on the learning data, and a memory unit that stores the learning data. The processing unit performs the learning process by performing a first process of rotating the magnet together with the rotor, a second process of acquiring, via the N3 third magnetic sensors, N3 analog signals whose electric signals vary according to magnetic field strength and have a third phase difference from one another, and a third process of acquiring, via the N2 second magnetic sensors, N2 analog signals whose electric signals vary according to magnetic field strength and have a second phase difference from one another, and a third process of calculating, based on the N3 analog signals acquired during a learning period corresponding to one period in mechanical angle, a time of the mechanical angle during the learning period. a fourth process of calculating time-series data; a fifth process of dividing the learning period into P pole pair regions linked to pole pair numbers representing the pole pair positions of the P magnetic pole pairs based on the N2 analog signals obtained during the learning period, further dividing each of the P pole pair regions into a plurality of sections, and linking each of the plurality of sections to a segment number representing the rotation position; and a sixth process of storing data representing the correspondence between the time-series data of the mechanical angle and the pole pair numbers in the storage unit as the learning data. As the position estimation process, the processing unit executes a seventh process of acquiring the N3 analog signals via the N3 third magnetic sensors, an eighth process of calculating a current value of the mechanical angle based on the N3 analog signals acquired in the seventh process, and a ninth process of determining, as an initial position of the rotor, a pole pair number corresponding to the current value of the mechanical angle based on the learning data stored in the memory unit.

[0010] Another aspect of the position estimation device of the present invention is a device for estimating the rotational position of a motor having a rotor with P (P is an integer greater than or equal to 2) magnetic pole pairs, comprising a magnet having one magnetic pole pair and sharing a rotation axis with the rotor, N4 (N4 is an integer greater than or equal to 3) fourth magnetic sensors facing the magnet and arranged along the rotation direction of the magnet, N2 (N2 is an integer greater than or equal to 3) second magnetic sensors facing the rotor and arranged along the rotation direction of the rotor, and a signal processing device that processes the output signals of the second magnetic sensors and the fourth magnetic sensors. The signal processing device has a processing unit that executes a learning process to acquire learning data necessary for estimating the rotational position and a position estimation process to estimate the rotational position of the rotor based on the learning data, and a memory unit that stores the learning data. The processing unit performs the learning process by performing a first process of rotating the magnet together with the rotor, a second process of acquiring, via the N4 fourth magnetic sensors, N4 analog signals whose electric signals vary according to magnetic field strength and have a fourth phase difference from one another, a third process of acquiring, via the N2 second magnetic sensors, N2 analog signals whose electric signals vary according to magnetic field strength and have a second phase difference from one another, a fourth process of dividing the learning period into a plurality of quadrants based on the N4 analog signals acquired during the learning period corresponding to one period in mechanical angle, and a fourth process of dividing the learning period into a plurality of quadrants based on the N4 analog signals acquired during the learning period. a fifth process of dividing the learning period into P pole pair regions linked to pole pair numbers representing pole pair positions of the P magnetic pole pairs based on the N2 analog signals obtained in step S1, further dividing each of the P pole pair regions into a plurality of sections, and linking each of the plurality of sections to a segment number representing the rotation position; and a sixth process of storing data indicating a correspondence between the segment numbers linked to the sections included in each of the plurality of quadrants and the pole pair numbers representing the pole pair positions in the storage unit as the learning data. As the position estimation process, the processing unit executes a seventh process of acquiring the N4 analog signals via the N4 fourth magnetic sensors, an eighth process of acquiring the N2 analog signals via the N2 second magnetic sensors, a ninth process of identifying a current quadrant from among the multiple quadrants based on the N4 analog signals acquired in the seventh process, a tenth process of identifying a current section from among the multiple sections based on the N2 analog signals acquired in the eighth process, and an eleventh process of determining, as an initial position of the rotor, a pole pair number corresponding to a segment number linked to the current section included in the current quadrant based on the learning data.

[0011] One aspect of the automated guided vehicle of the present invention comprises a motor having a rotor with P magnetic pole pairs (P is an integer greater than or equal to 2), and a position estimation device according to any one of the above three aspects that estimates the rotational position of the motor.

[0012] One aspect of the sewing device of the present invention comprises a motor having a rotor with P magnetic pole pairs (P is an integer greater than or equal to 2), and a position estimation device according to any one of the above three aspects that estimates the rotational position of the motor. [Effects of the Invention]

[0013] According to the above aspects of the present invention, there are provided a position estimation method, a position estimation device, an automated guided vehicle, and a sewing device that can eliminate the need for a preliminary rotational operation for estimating a rotational position. [Brief explanation of the drawings]

[0014] [Figure 1] FIG. 1 is a block diagram schematically showing the configuration of a position estimation device according to a first embodiment of the present invention. [Figure 2] FIG. 2 is a flowchart showing the learning process executed by the processing unit in the first embodiment. [Figure 3] FIG. 3 is an explanatory diagram relating to learning data acquired by the learning process in the first embodiment. [Figure 4] FIG. 4 is an enlarged view of the incremental signals Hu, Hv, and Hw contained in one pole pair region. [Figure 5] FIG. 5 is a flowchart showing the position estimation process executed by the processing unit in the first embodiment. [Figure 6] FIG. 6 is a diagram showing a modified example of the position estimation device in the first embodiment. [Figure 7] FIG. 7 is a block diagram schematically showing the configuration of a position estimation device according to the second embodiment of the present invention. [Figure 8] FIG. 8 is a flowchart showing the learning process executed by the processing unit in the second embodiment. [Figure 9] FIG. 9 is an explanatory diagram relating to learning data acquired by the learning process in the second embodiment. [Figure 10] FIG. 10 is a flowchart showing the position estimation process executed by the processing unit in the second embodiment. [Figure 11]FIG. 11 is a block diagram schematically showing the configuration of a position estimation device according to the third embodiment of the present invention. [Figure 12] FIG. 12 is a flowchart showing the learning process executed by the processing unit in the third embodiment. [Figure 13] FIG. 13 is an explanatory diagram relating to learning data acquired by the learning process in the third embodiment. [Figure 14] FIG. 14 is a flowchart showing the position estimation process executed by the processing unit in the third embodiment. [Figure 15] FIG. 15 is a diagram showing a first modified example of the third embodiment. [Figure 16] FIG. 16 is a diagram showing a second modified example of the third embodiment. [Figure 17] FIG. 17 is a diagram showing the appearance of an automatic guided vehicle to which the present invention is applied. [Figure 18] FIG. 18 is a diagram showing the appearance of a sewing device to which the present invention is applied. DETAILED DESCRIPTION OF THE INVENTION

[0015] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. [First embodiment] FIG. 1 is a block diagram schematically illustrating the configuration of a position estimation device 100 according to a first embodiment of the present invention. As shown in FIG. 1, the position estimation device 100 is a device that estimates the rotational position (rotation angle) of a motor 200 that includes a rotor 210 having P magnetic pole pairs (P is an integer equal to or greater than 2). In this embodiment, as an example, the rotor 210 has four magnetic pole pairs. Note that a magnetic pole pair refers to a pair of an N pole and an S pole. That is, in this embodiment, the rotor 210 has four pairs of an N pole and an S pole, for a total of eight magnetic poles (rotor magnets).

[0016] The motor 200 is, for example, an inner rotor type three-phase brushless DC motor. Although not shown in FIG. 1, the motor 200 also has a stator and a motor housing in addition to the rotor 210. The motor housing accommodates the rotor 210 and the stator inside. The rotor 210 is supported by bearing components inside the motor housing so as to be rotatable around a rotation axis. The stator has three-phase excitation coils including a U-phase coil, a V-phase coil, and a W-phase coil, and is fixed inside the motor housing so as to face the outer circumferential surface of the rotor 210.

[0017] The position estimation device 100 includes a sensor magnet 10, three first magnetic sensors 21, 22, and 23, three second magnetic sensors 31, 32, and 33, and a signal processing device 40. Although not shown in Fig. 1, a circuit board is attached to the motor 200, and the first magnetic sensors 21, 22, and 23, the second magnetic sensors 31, 32, and 33, and the signal processing device 40 are arranged on the circuit board.

[0018] The sensor magnet 10 is a disk-shaped magnet that has one magnetic pole pair and shares a common rotation axis with the rotor 210. When the rotor 210 rotates, the sensor magnet 10 rotates in synchronization with the rotor 210. The sensor magnet 10 is disposed in a position that does not interfere with the circuit board. The sensor magnet 10 may be disposed inside or outside the motor housing.

[0019] The first magnetic sensors 21, 22, and 23 are magnetic sensors that face the sensor magnet 10 and are arranged at predetermined intervals on the circuit board along the rotation direction of the sensor magnet 10. In this embodiment, the position estimation device 100 includes three first magnetic sensors 21, 22, and 23, but the number of first magnetic sensors may be N1 (N1 is an integer equal to or greater than 3). For example, the first magnetic sensors 21, 22, and 23 are each Hall ICs that incorporate a Hall element, a latch circuit, and the like. The first magnetic sensors 21, 22, and 23 each output a digital signal whose level is inverted every time the sensor magnet 10 rotates 180°.

[0020] In this embodiment, the first magnetic sensors 21, 22, and 23 are arranged at 120° intervals along the rotation direction of the sensor magnet 10. Therefore, the digital signals output from the first magnetic sensors 21, 22, and 23 have a phase difference (first phase difference) of 120° electrical angle from one another. Hereinafter, the digital signals output from the first magnetic sensors 21, 22, and 23 will be referred to as absolute digital signals. The first magnetic sensor 21 outputs an absolute digital signal HA1 to the signal processing device 40. The first magnetic sensor 22 outputs an absolute digital signal HA2 to the signal processing device 40. The first magnetic sensor 23 outputs an absolute digital signal HA3 to the signal processing device 40.

[0021] The second magnetic sensors 31, 32, and 33 are magnetic sensors that face the rotor 210 and are arranged at predetermined intervals on the circuit board along the rotation direction of the rotor 210. In this embodiment, the position estimation device 100 includes three second magnetic sensors 31, 32, and 33. However, the number of second magnetic sensors may be N2 (N2 is an integer equal to or greater than 3). For example, the second magnetic sensors 31, 32, and 33 are each a Hall element or a linear Hall IC. The second magnetic sensors 31, 32, and 33 each output an analog signal whose electrical signal fluctuates according to magnetic field strength. One electrical angle cycle of each analog signal corresponds to 1 / P of one mechanical angle cycle. In this embodiment, since the number of pole pairs P of the rotor 210 is "4," one electrical angle cycle of each analog signal corresponds to 1 / 4 of one mechanical angle cycle, i.e., 90° mechanical angle.

[0022] In this embodiment, the second magnetic sensors 31, 32, and 33 are arranged at 30° intervals along the rotation direction of the rotor 210. Therefore, the analog signals output from the second magnetic sensors 31, 32, and 33 have a phase difference (second phase difference) of 120° electrical angle from one another. Hereinafter, the analog signals output from the second magnetic sensors 31, 32, and 33 will be referred to as incremental signals. The second magnetic sensor 31 outputs an incremental signal Hu to the signal processing device 40. The second magnetic sensor 32 outputs an incremental signal Hv to the signal processing device 40. The second magnetic sensor 33 outputs an incremental signal Hw to the signal processing device 40.

[0023] The signal processing device 40 processes output signals from the first magnetic sensors 21, 22, and 23 and the second magnetic sensors 31, 32, and 33. The signal processing device 40 estimates the rotational position of the motor 200, i.e., the rotational position of the rotor 210, based on the absolute digital signals HA1, HA2, and HA3 and the incremental signals Hu, Hv, and Hw. The signal processing device 40 includes a processing unit 41 and a storage unit 42.

[0024] The processing unit 41 is a microprocessor such as an MCU (Microcontroller Unit). Absolute digital signals HA1, HA2, and HA3 and incremental signals Hu, Hv, and Hw are input to the processing unit 41. The processing unit 41 is connected to a storage unit 42 via a data bus so as to be able to communicate data with the storage unit 42.

[0025] The incremental signals Hu, Hv, and Hw are converted into digital signals via an A / D converter inside the processing unit 41, but for convenience of explanation, the digital signals output from the A / D converter are also referred to as incremental signals Hu, Hv, and Hw. In the following explanation, the absolute digital signals HA1, HA2, and HA3 input to the processing unit 41 and the incremental signals Hu, Hv, and Hw may be collectively referred to as "input sensor signals."

[0026] The processing unit 41 executes at least the following two processes in accordance with the programs stored in the storage unit 42. The processing unit 41 executes a learning process to acquire learning data necessary for estimating the rotational position of the rotor 210, based on the input sensor signal. The processing unit 41 executes a position estimation process to estimate the rotational position of the rotor 210, based on the input sensor signal and the learning data.

[0027] The storage unit 42 includes a nonvolatile memory that stores programs, various setting values, learning data, and the like required for the processing unit 41 to execute various processes, and a volatile memory that is used as a temporary storage destination for data when the processing unit 16 executes various processes. The nonvolatile memory is, for example, an EEPROM (Electrically Erasable Programmable Read-Only Memory) or a flash memory. The volatile memory is, for example, a RAM (Random Access Memory).

[0028] Next, the learning process executed by the processing unit 41 will be described. The learning process corresponds to the learning step in the position estimation method of claim 1. Fig. 2 is a flowchart showing the learning process executed by the processing unit 41 in the first embodiment. The processing unit 41 executes the learning process shown in Fig. 2 when the power supply of the signal processing device 40, which executes processes according to at least the learning step and the position estimation step, is turned on for the first time.

[0029] 2, when the processing unit 41 starts the learning process, it first executes a first process of rotating the sensor magnet 10 together with the rotor 210 (step S1). This first process corresponds to the first step of the learning step in the position estimation method of claim 1.

[0030] Next, the processing unit 41 executes a second process (step S2) to acquire three absolute digital signals HA1, HA2, and HA3 via the three first magnetic sensors 21, 22, and 23. This second process corresponds to the second learning step in the position estimation method of claim 1.

[0031] As shown in Fig. 3, the absolute digital signals HA1, HA2, and HA3 are digital signals whose levels are inverted every time the sensor magnet 10 rotates 180° and which have a phase difference of 120° electrical angle from one another. In Fig. 3, the period from time t1 to time t9 corresponds to one mechanical angle cycle. In Fig. 3, the period from time t1 to time t2, the period from time t2 to time t4, the period from time t4 to time t5, the period from time t5 to time t6, the period from time t6 to time t8, and the period from time t8 to time t9 each correspond to 1 / 6 of one mechanical angle cycle, or 60° mechanical angle.

[0032] Next, the processing unit 41 executes a third process (step S3) to acquire three incremental signals Hu, Hv, and Hw via the three second magnetic sensors 31, 32, and 33. This third process corresponds to the third step of the learning step in the position estimation method of claim 1.

[0033] As shown in Fig. 3, one electrical angle cycle of each of the incremental signals Hu, Hv, and Hw corresponds to ¼ of one mechanical angle cycle, i.e., 90° mechanical angle. In Fig. 3, the periods from time t1 to time t3, from time t3 to time t5, from time t5 to time t7, and from time t7 to time t9 each correspond to 90° mechanical angle. In addition, the incremental signals Hu, Hv, and Hw have a phase difference of 120° electrical angle from one another.

[0034] Next, the processing unit 41 executes a fourth process of dividing the learning period into a plurality of quadrants each having a different N1-bit digital value based on the three absolute digital signals HA1, HA2, and HA3 obtained during the learning period corresponding to one mechanical angle cycle (step S4). This fourth process corresponds to the fourth step of the learning step in the position estimation method of claim 1.

[0035] "N1" is the number of first magnetic sensors. In this embodiment, since the number of first magnetic sensors is three, in step S4, the processing unit 41 divides the learning period into a plurality of quadrants each having a different 3-bit digital value. In this embodiment, of the 3-bit digital values, the value of the most significant bit is the value of absolute digital signal HA1, the value of the middle bit is the value of absolute digital signal HA2, and the value of the least significant bit is the value of absolute digital signal HA3.

[0036] As shown in FIG. 3, the processing unit 41 divides the learning period (one mechanical angle cycle) into six quadrants based on the absolute digital signals HA1, HA2, and HA3. The processing unit 41 divides the learning period from time t1 to time t2 into a first quadrant having a 3-bit digital value "101." The processing unit 41 divides the learning period from time t2 to time t4 into a second quadrant having a 3-bit digital value "100." The processing unit 41 divides the learning period from time t4 to time t5 into the third quadrant having a 3-bit digital value "110". The processing unit 41 divides the learning period from time t5 to time t6 into a fourth quadrant having a 3-bit digital value "010". The processing unit 41 divides the learning period from time t6 to time t8 into a fifth quadrant having a 3-bit digital value "011". The processing unit 41 divides the learning period from time t8 to time t9 into a sixth quadrant having a 3-bit digital value "001."

[0037] Next, the processing unit 41 executes a fifth process (step S5) in which, based on the three incremental signals Hu, Hv, and Hw obtained during the learning period, the learning period is divided into four pole pair regions associated with pole pair numbers representing the pole pair positions of the four magnetic pole pairs, each of the four pole pair regions is further divided into a plurality of sections, and a segment number representing the rotational position of the rotor 210 is associated with each of the plurality of sections. This fifth process corresponds to the fifth step of the learning step in the position estimation method of claim 1.

[0038] In this embodiment, to estimate the rotational position of rotor 210, pole pair numbers indicating the pole pair positions are assigned to the four magnetic pole pairs of rotor 210. For example, as shown in Fig. 1, pole pair numbers "0," "1," "2," and "3" are assigned to the four magnetic pole pairs of rotor 210 in clockwise order.

[0039] 3, in step S5, the processing unit 41 divides the learning period into four pole pair regions based on the three incremental signals Hu, Hv, and Hw obtained during the learning period. In FIG. 3, "No. C" indicates the pole pair number. The processing unit 41 divides the period from time t1 to time t3 of the learning period into a pole pair region associated with pole pair number "0". The processing unit 41 divides the period from time t3 to time t5 of the learning period into a pole pair region associated with pole pair number "1". The processing unit 41 divides the period from time t5 to time t7 of the learning period into a pole pair region associated with pole pair number "2". The processing unit 41 divides the period from time t7 to time t9 of the learning period into a pole pair region associated with the pole pair number "3".

[0040] 3, in step S5, processing unit 41 further divides each of the four pole pair regions into 12 sections based on the three incremental signals Hu, Hv, and Hw obtained during the learning period, and associates each of the 12 sections with a segment number that indicates the rotational position of rotor 210. In FIG. 3, "No. A" indicates the section number assigned to the section, and "No. B" indicates the segment number.

[0041] As shown in Figure 3, the 12 sections in each of the four pole pair regions are assigned section numbers from "0" to "11." Consecutive numbers are associated with each section throughout the entire learning period as segment numbers. Specifically, as shown in Figure 3, in the pole pair region associated with pole pair number "0," segment numbers "0" to "11" are associated with section numbers "0" to "11." In the pole pair region associated with pole pair number "1," segment numbers "12" to "23" are associated with section numbers "0" to "11." In the pole pair region associated with pole pair number "2," segment numbers "24" to "35" are associated with section numbers "0" to "11." In the pole pair region associated with pole pair number "3," segment numbers "36" to "47" are associated with section numbers "0" to "11."

[0042] FIG. 4 is an enlarged view of the incremental signals Hu, Hv, and Hw contained in one pole pair region. Hereinafter, with reference to FIG. 4, a process of dividing each of the four pole pair regions into 12 sections, which is included in the fifth process executed by the processing unit 41, will be specifically described. In FIG. 4, the reference value of the amplitude is "0." In FIG. 4, a positive digital value of the amplitude represents, as an example, the digital value of the magnetic field strength of the north pole. Also, a negative digital value of the amplitude represents, as an example, the digital value of the magnetic field strength of the south pole.

[0043] In the fifth step of the learning process, the processing unit 41 executes a process of extracting zero-crossing points, which are points where the three incremental signals Hu, Hv, and Hw included in each of the four pole pair regions cross a reference value of 0. As shown in Fig. 4, the processing unit 41 extracts points P1, P3, P5, P7, P9, P11, and P13 as the zero-crossing points.

[0044] In the fifth step of the learning process, the processing unit 41 executes a process of extracting intersections, which are points where the three incremental signals Hu, Hv, and Hw included in each of the four pole pair regions intersect with each other. As shown in Fig. 4, the processing unit 41 extracts points P2, P4, P6, P8, P10, and P12 as the intersections.

[0045] In the fifth process of the learning process, the processing unit 41 executes a process of determining an interval between adjacent zero crossing points and intersection points as a section. As shown in FIG. 4, the processing unit 41 determines the section between the zero crossing point P1 and the intersection point P2 as the section to be assigned the section number "0". The processing unit 41 determines the section between the intersection point P2 and the zero cross point P3 as the section to be assigned the section number "1". The processing unit 41 determines the section between the zero crossing point P3 and the intersection point P4 as the section to be assigned the section number "2". The processing unit 41 determines the section between the intersection point P4 and the zero cross point P5 as the section to be assigned the section number "3". The processing unit 41 determines the section between the zero crossing point P5 and the intersection point P6 as the section to be assigned the section number "4". The processing unit 41 determines the section between the intersection point P6 and the zero cross point P7 as the section to be assigned the section number "5".

[0046] The processing unit 41 determines the section between the zero crossing point P7 and the intersection point P8 as the section to be assigned the section number "6". The processing unit 41 determines the section between the intersection point P8 and the zero cross point P9 as the section to be assigned the section number "7". The processing unit 41 determines the section between the zero crossing point P9 and the intersection point P10 as the section to be assigned the section number "8". The processing unit 41 determines the section between the intersection point P10 and the zero cross point P11 as the section to be assigned the section number "9". The processing unit 41 determines the section between the zero crossing point P11 and the intersection point P12 as the section to be assigned the section number "10". The processing unit 41 determines the section between the intersection point P12 and the zero cross point P13 as the section to be assigned the section number "11".

[0047] By performing the fifth step of the learning process described above, the learning period is divided into four pole pair regions linked to pole pair numbers, and each of the four pole pair regions is divided into 12 sections, and each section is linked to a segment number, as shown in Figure 3. In the following description, for example, a section assigned section number "0" will be referred to as "section 0," and a section assigned section number "11" will be referred to as "section 11."

[0048] Next, the processing unit 41 executes a sixth process of acquiring data indicating the correspondence between the segment numbers associated with the sections included in each of the six quadrants and the pole pair numbers indicating the pole pair positions as learning data, and storing the acquired learning data in the storage unit 42 (step S6). This sixth process corresponds to the sixth step of the learning step in the position estimation method of claim 1.

[0049] As shown in Figure 3, the first quadrant includes eight sections, from section 0 to section 7. The learning data includes data indicating the correspondence between segment numbers "0" to "7" associated with sections 0 to 7 included in the first quadrant and pole pair number "0."

[0050] As shown in FIG. 3, the second quadrant includes four sections, section 8 to section 11, and four sections, section 0 to section 3. The learning data includes data indicating the correspondence between segment numbers "8" to "11" associated with sections 8 to 11 included in the second quadrant and pole pair number "0." The learning data also includes data indicating the correspondence between segment numbers "12" to "15" associated with sections 0 to 3 included in the second quadrant and pole pair number "1."

[0051] As shown in Figure 3, the third quadrant includes eight sections, from section 4 to section 11. The training data includes data indicating the correspondence between segment numbers "16" to "23" associated with sections 4 to 11 included in the third quadrant and pole pair number "1."

[0052] As shown in Figure 3, the fourth quadrant includes eight sections, from section 0 to section 7. The training data includes data indicating the correspondence between segment numbers "24" to "31" associated with sections 0 to 7 included in the fourth quadrant and pole pair number "2."

[0053] As shown in FIG. 3, the fifth quadrant includes four sections, section 8 to section 11, and four sections, section 0 to section 3. The learning data includes data indicating the correspondence between segment numbers "32" to "35" associated with sections 8 to 11 included in the fifth quadrant and pole pair number "2." The learning data also includes data indicating the correspondence between segment numbers "36" to "39" associated with sections 0 to 3 included in the fifth quadrant and pole pair number "3."

[0054] As shown in Figure 3, the sixth quadrant includes eight sections, from section 4 to section 11. The training data includes data indicating the correspondence between segment numbers "40" to "47" associated with sections 4 to 11 included in the sixth quadrant and pole pair number "3."

[0055] Next, a description will be given of the position estimation process executed by the processing unit 41. The position estimation process corresponds to the position estimation step in the position estimation method of claim 1. Fig. 5 is a flowchart showing the position estimation process executed by the processing unit 41 in the first embodiment. After executing the above learning process, the processing unit 41 executes the position estimation process shown in Fig. 5 when the power of the signal processing device 40 is turned on again.

[0056] 5, when the processing unit 41 starts the position estimation process, first, it executes a seventh process (step S7) to acquire three absolute digital signals HA1, HA2, and HA3 via the three first magnetic sensors 21, 22, and 23. This seventh process corresponds to the seventh step of the position estimation step in the position estimation method of claim 1.

[0057] Subsequently, the processing unit 41 executes an eighth process (step S8) to acquire three incremental signals Hu, Hv, and Hw via the three second magnetic sensors 31, 32, and 33. This eighth process corresponds to the eighth step of the position estimation step in the position estimation method of claim 1.

[0058] Next, the processing unit 41 executes a ninth process (step S9) to identify the current quadrant from the six quadrants based on the three absolute digital signals HA1, HA2, and HA3 acquired in the seventh process (step S7). This ninth process corresponds to the ninth step of the position estimation step in the position estimation method of claim 1.

[0059] In step S9, the processing unit 41 identifies the current quadrant from among the six quadrants based on the 3-bit digital value represented by the absolute digital signals HA1, HA2, and HA3. For example, if the 3-bit digital value is "100," the processing unit 41 identifies the second quadrant as the current quadrant.

[0060] Next, the processing unit 41 executes a tenth process (step S10) to identify the current section from among the 12 sections based on the three incremental signals Hu, Hv, and Hw acquired in the eighth process (step S8). This tenth process corresponds to the tenth step of the position estimation step in the position estimation method of claim 1.

[0061] In step S10, the processing unit 41 identifies the current section from among the 12 sections based on, for example, the magnitude relationship between the detected values ​​of the incremental signals Hu, Hv, and Hw and the positive and negative signs of each detected value. As shown in FIG. 4, for example, in section 2, the detected value of the incremental signal Hu is the largest and has a positive sign. Also, in section 2, the detected value of the incremental signal Hw is the second largest and has a negative sign. Also, in section 2, the detected value of the incremental signal Hv is the smallest and has a negative sign. If the magnitude relationship between the detected values ​​of the incremental signals Hu, Hv, and Hw and the positive and negative signs of each detected value satisfy the above-mentioned conditions for section 2, the processing unit 41 identifies section 2 as the current section.

[0062] Next, the processing unit 41 executes an eleventh process (step S11) to determine, based on the learning data stored in the storage unit 42, the pole pair number corresponding to the segment number associated with the current section included in the current quadrant as the initial position of the rotor 210. This eleventh process corresponds to the eleventh step of the position estimation step in the position estimation method of claim 1.

[0063] For example, assume that the second quadrant is identified as the current quadrant and the second section is identified as the current section, as described above. As shown in Fig. 3, the learning data includes data indicating the correspondence between segment numbers "12" to "15" associated with sections 0 to 3 included in the second quadrant and pole pair number "1." Therefore, when the second quadrant is identified as the current quadrant and section 2 is identified as the current section, processing unit 41 determines, as the initial position of rotor 210, pole pair number "1," which corresponds to segment number "14" associated with section 2 included in the second quadrant.

[0064] As described above, the position estimation device 100 of the first embodiment includes a processing unit 41 that executes a learning process to acquire learning data necessary for estimating the rotational position of the rotor 210 based on input sensor signals, and a position estimation process to estimate the rotational position of the rotor 210 based on the input sensor signals and the learning data. The processing unit 41 executes the learning process at least when the signal processing device 40 is powered on for the first time, thereby acquiring data indicating the correspondence between segment numbers associated with sections included in each of the six quadrants and pole pair numbers indicating pole pair positions as learning data. The processing unit 41 executes the position estimation process when the signal processing device 40 is powered on again, thereby determining the initial position of the rotor 210.

[0065] As a result, the position estimation device 100 of the first embodiment can estimate the initial position of the rotor 210 without rotating the rotor 210. Therefore, the motor 200 equipped with the position estimation device 100 does not need to adjust the origin of the rotational position of the rotor 210 when powered on. Because the motor 200 does not require a preliminary rotation operation for adjusting the origin, it can also be suitably used as a drive motor for robots, automatic guided vehicles, and the like, which are not permitted to perform preliminary rotation operation. Because the motor 200 does not require a preliminary rotation operation for adjusting the origin, the drive time and power consumption required for the preliminary rotation operation can be reduced.

[0066] (Modification of the first embodiment) The present invention is not limited to the first embodiment, and the configurations described in this specification can be combined as appropriate within a range that does not contradict each other. In the first embodiment described above, the first magnetic sensors 21, 22, and 23 are each a Hall IC incorporating a Hall element and a latch circuit, etc. For example, as shown in Fig. 6, the first magnetic sensors 21, 22, and 23 may each be replaced with a Hall element, and a comparator circuit 44 that converts analog signals output from the three Hall elements into absolute digital signals HA1, HA2, and HA3 may be provided in the signal processing device 40. The comparator circuit 44 may be provided outside the processing unit 41 or inside the processing unit 41.

[0067] In addition, in the above first embodiment, an example was given of a case where three first magnetic sensors that output absolute digital signals are provided, but the number of first magnetic sensors is not limited to three, and the number of first magnetic sensors may be N1 (N1 is an integer greater than or equal to 3). In addition, in the above first embodiment, an example was given of a case where three second magnetic sensors that output incremental signals are provided, but the number of second magnetic sensors is not limited to three, and the number of second magnetic sensors may be N2 (N2 is an integer greater than or equal to 3). In addition, in the above first embodiment, a motor having a rotor with four magnetic pole pairs was exemplified, but the number of pole pairs of the rotor is not limited to four, and the number of pole pairs of the rotor may be P (P is an integer greater than or equal to 2).

[0068] Second Embodiment Next, a second embodiment of the present invention will be described. Fig. 7 is a block diagram schematically showing the configuration of a position estimation device 110 in a second embodiment of the present invention. As shown in Fig. 7, the position estimation device 110 is a device that estimates the rotational position (rotation angle) of a motor 200 that includes a rotor 210 having P magnetic pole pairs (P is an integer equal to or greater than 2). In this embodiment, as an example, the rotor 210 has four magnetic pole pairs. Since the configuration of the motor 200 is the same as in the first embodiment, a description of the motor 200 in the second embodiment will be omitted.

[0069] The position estimation device 110 includes a sensor magnet 10, two third magnetic sensors 51 and 52, three second magnetic sensors 31, 32, and 33, and a signal processing device 40. Although not shown in Fig. 7, a circuit board is attached to the motor 200, and the third magnetic sensors 51 and 52, the second magnetic sensors 31, 32, and 33, and the signal processing device 40 are arranged on the circuit board.

[0070] The sensor magnet 10 is the same as in the first embodiment. That is, the sensor magnet 10 is a disk-shaped magnet that has one magnetic pole pair and shares a rotation axis with the rotor 210. When the rotor 210 rotates, the sensor magnet 10 rotates in synchronization with the rotor 210.

[0071] The third magnetic sensors 52 and 53 are magnetic sensors that face the sensor magnet 10 on the circuit board and are arranged at a predetermined interval along the rotation direction of the sensor magnet 10. In this embodiment, the position estimation device 110 includes two third magnetic sensors 51 and 52, but the number of third magnetic sensors may be N3 (N3 is an integer equal to or greater than 2). For example, the third magnetic sensors 51 and 52 are each a Hall element or a linear Hall IC. The third magnetic sensors 51 and 52 each output an analog signal, the electrical signal of which fluctuates according to the magnetic field strength. One electrical angle cycle of the analog signal output from the third magnetic sensors 51 and 52 corresponds to one mechanical angle cycle.

[0072] In this embodiment, the third magnetic sensors 51 and 52 are arranged at 90° intervals along the rotation direction of the sensor magnet 10. Therefore, the analog signals output from the third magnetic sensors 51 and 52 have a phase difference (third phase difference) of 90° electrical angle from each other. Hereinafter, the analog signals output from the third magnetic sensors 51 and 52 will be referred to as absolute analog signals. The third magnetic sensor 51 outputs an absolute analog signal HB1 to the signal processing device 40. The third magnetic sensor 52 outputs an absolute analog signal HB2 to the signal processing device 40.

[0073] The second magnetic sensors 31, 32, and 33 are magnetic sensors that are arranged on the circuit board facing the rotor 210 and at predetermined intervals along the rotation direction of the rotor 210. The second magnetic sensors 31, 32, and 33 are the same as those in the first embodiment, and therefore a description of the second magnetic sensors 31, 32, and 33 in the second embodiment will be omitted.

[0074] The signal processing device 40 of the second embodiment estimates the rotational position of the motor 200, i.e., the rotational position of the rotor 210, based on the absolute analog signals HB1 and HA2 output from the third magnetic sensors 51 and 52 and the incremental signals Hu, Hv, and Hw output from the second magnetic sensors 31, 32, and 33. The signal processing device 40 includes a processing unit 41 and a storage unit 42. The storage unit 42 is the same as in the first embodiment, and therefore a description of the storage unit 42 will be omitted in the second embodiment.

[0075] Absolute analog signals HB1 and HB2 and incremental signals Hu, Hv, and Hw are input to processing unit 41. Absolute analog signals HB1 and HB2 and incremental signals Hu, Hv, and Hw are converted into digital signals via an A / D converter inside processing unit 41, but for convenience of explanation, the digital signals output from the A / D converter will also be referred to as absolute analog signals HB1 and HB2 and incremental signals Hu, Hv, and Hw. In the following explanation, absolute analog signals HB1 and HB2 and incremental signals Hu, Hv, and Hw input to processing unit 41 may be collectively referred to as "input sensor signals."

[0076] The processing unit 41 executes at least the following two processes in accordance with the program stored in the storage unit 42. The processing unit 41 executes a learning process to acquire learning data necessary for estimating the rotational position of the rotor 210 based on the input sensor signal. The processing unit 41 executes a position estimation process to estimate the rotational position of the rotor 210 based on the input sensor signal and the learning data. In the second embodiment, the contents of the learning process and the position estimation process executed by the processing unit 41 differ from those in the first embodiment.

[0077] Next, the learning process executed by the processing unit 41 of the second embodiment will be described. The learning process corresponds to the learning step in the position estimation method of claim 2. Fig. 8 is a flowchart showing the learning process executed by the processing unit 41 in the second embodiment. The processing unit 41 executes the learning process shown in Fig. 8 at least when the power of the signal processing device 40 is turned on for the first time.

[0078] 8, when the processing unit 41 starts the learning process, first, it executes a first process of rotating the sensor magnet 10 together with the rotor 210 (step S21). This first process corresponds to the first step of the learning step in the position estimation method of claim 2.

[0079] Subsequently, the processing unit 41 executes a second process of acquiring two absolute analog signals HB1 and HB2 via the two third magnetic sensors 51 and 52 (step S22). This second process corresponds to the second learning step in the position estimation method of claim 2.

[0080] As shown in Fig. 9, one electrical cycle of each of absolute analog signals HB1 and HB2 corresponds to one mechanical cycle. In Fig. 9, the period from time t1 to time t9 corresponds to one mechanical cycle. The absolute analog signals HB1 and HB2 have a phase difference of 90° electrical angle from each other.

[0081] Subsequently, the processing unit 41 executes a third process (step S23) to acquire three incremental signals Hu, Hv, and Hw via the three second magnetic sensors 31, 32, and 33. This third process corresponds to the third step of the learning step in the position estimation method of claim 2.

[0082] As shown in Fig. 9, similar to the first embodiment, one electrical angle cycle of each of the incremental signals Hu, Hv, and Hw corresponds to ¼ of one mechanical angle cycle, i.e., 90° mechanical angle. In Fig. 3, the periods from time t1 to time t3, from time t3 to time t5, from time t5 to time t7, and from time t7 to time t9 each correspond to 90° mechanical angle. Furthermore, the incremental signals Hu, Hv, and Hw have a phase difference of 120° electrical angle from one another.

[0083] Next, the processing unit 41 executes a fourth process of calculating time-series data of the mechanical angle θ during the learning period based on the two absolute analog signals HB1 and HB2 obtained during the learning period corresponding to one cycle of the mechanical angle (step S24). This fourth process corresponds to the fourth step of the learning step in the position estimation method of claim 2.

[0084] For example, in step S24, the processing unit 41 samples the absolute analog signals HB1 and HB2 obtained during the learning period at a predetermined sampling frequency, and calculates time-series data of the mechanical angle θ by substituting the sampled values ​​of the absolute analog signal HB1 and the sampled values ​​of the absolute analog signal HB2 into the following arithmetic expression (1). Hereinafter, the time-series data of the mechanical angle θ will be referred to as mechanical angle time-series data. Mechanical angle θ=tan -1 (HB1 / HB2) …(1)

[0085] Next, the processing unit 41 executes a fifth process (step S25) based on the three incremental signals Hu, Hv, and Hw obtained during the learning period, in which the learning period is divided into four pole pair regions associated with pole pair numbers representing the pole pair positions of the four magnetic pole pairs, each of the four pole pair regions is further divided into a plurality of sections, and a segment number representing the rotational position of the rotor 210 is associated with each of the plurality of sections. This fifth process corresponds to the fifth step of the learning step in the position estimation method of claim 2.

[0086] By performing the processing of step S25, the learning period is divided into four pole pair regions linked to pole pair numbers, each of the four pole pair regions is divided into 12 sections, and each section is linked to a segment number, as shown in Fig. 9. The processing of step S25 is similar to step S5 of the learning processing in the first embodiment, so a description of step S25 in the second embodiment will be omitted.

[0087] Subsequently, the processing unit 41 executes a sixth process of acquiring data indicating the correspondence between the mechanical angle time-series data and the pole pair numbers as learning data and storing the acquired learning data in the storage unit 42 (step S26). This sixth process corresponds to the sixth step of the learning step in the position estimation method of claim 2.

[0088] For example, as shown in FIG. 9 , in the learning data, the pole pair number "0" is associated with the mechanical angle θ from 0° (360°) to 89° in the mechanical angle time series data. In the learning data, the pole pair number "1" is associated with the mechanical angle θ from 90° to 179° in the mechanical angle time series data. In the learning data, the pole pair number "2" is associated with the mechanical angle θ from 180° to 269° in the mechanical angle time series data. In the learning data, the pole pair number "3" is associated with the mechanical angle θ from 270° to 359° in the mechanical angle time series data.

[0089] Next, a description will be given of the position estimation process executed by the processing unit 41 in the second embodiment. The position estimation process in the second embodiment corresponds to the position estimation step in the position estimation method of claim 2. Fig. 10 is a flowchart showing the position estimation process executed by the processing unit 41 in the second embodiment. After executing the learning process shown in Fig. 8, the processing unit 41 executes the position estimation process shown in Fig. 10 when the power of the signal processing device 40 is turned on again.

[0090] 10, when the processing unit 41 starts the position estimation process, first, it executes a seventh process (step S27) to acquire two absolute analog signals HB1 and HA2 via the two third magnetic sensors 51 and 52. This seventh process corresponds to the seventh step of the position estimation step in the position estimation method of claim 2.

[0091] Next, the processing unit 41 executes an eighth process (step S28) for calculating the current value of the mechanical angle θ based on the two absolute analog signals HB1 and HA2 acquired in the seventh process (step S27). This eighth process corresponds to the eighth step of the position estimation step in the position estimation method of claim 2. In step S28, the processing unit 41 calculates the current value of the mechanical angle θ by substituting the sampled value of the absolute analog signal HB1 and the sampled value of the absolute analog signal HB2 into the above-mentioned arithmetic expression (1).

[0092] Next, the processing unit 41 executes a ninth process (step S29) to determine the pole pair number corresponding to the current value of the mechanical angle θ as the initial position of the rotor 210, based on the learning data stored in the storage unit 42. This ninth process corresponds to the ninth step of the position estimation step in the position estimation method of claim 2.

[0093] For example, assume that a value within the range of 90° to 179° is calculated as the current value of the mechanical angle θ. As described above, in the learning data, the pole pair number "1" is associated with the mechanical angle θ from 90° to 179° in the mechanical angle time-series data. Therefore, when a value within the range of 90° to 179° is calculated as the current value of the mechanical angle θ, the processing unit 41 determines the pole pair number "1" corresponding to the current value of the mechanical angle θ as the initial position of the rotor 210.

[0094] As described above, the position estimation device 110 of the second embodiment includes a processing unit 41 that executes a learning process that acquires learning data necessary for estimating the rotational position of the rotor 210 based on input sensor signals, and a position estimation process that estimates the rotational position of the rotor 210 based on the input sensor signals and the learning data. The processing unit 41 executes the learning process at least when the signal processing device 40 is powered on for the first time, thereby acquiring data indicating the correspondence between the mechanical angle time-series data and the pole pair numbers as learning data. The processing unit 41 executes the position estimation process when the signal processing device 40 is powered on again, thereby determining the initial position of the rotor 210.

[0095] As a result, similar to the first embodiment, the position estimation device 110 of the second embodiment can estimate the initial position of the rotor 210 without rotating the rotor 210. Therefore, the motor 200 equipped with the position estimation device 110 does not need to adjust the origin of the rotational position of the rotor 210 when powered on. Because the motor 200 does not require a preliminary rotation operation for adjusting the origin, it can also be suitably used as a drive motor for robots, automatic guided vehicles, and the like, which are not permitted to perform preliminary rotation operation. Because the motor 200 does not require a preliminary rotation operation for adjusting the origin, the drive time and power consumption required for the preliminary rotation operation can be reduced.

[0096] (Modification of the second embodiment) The present invention is not limited to the second embodiment, and the configurations described in this specification can be combined as appropriate within a range that does not contradict each other. In the second embodiment, the case where two third magnetic sensors that output absolute analog signals are provided is exemplified, but the number of third magnetic sensors is not limited to two, and may be N3 (N3 is an integer equal to or greater than 2) third magnetic sensors. In other words, the number of third magnetic sensors may be three or more. In addition, in the above second embodiment, an example was given of a case where three second magnetic sensors that output incremental signals are provided, but the number of second magnetic sensors is not limited to three, and the number of second magnetic sensors may be N2 (N2 is an integer greater than or equal to 3). In addition, in the above second embodiment, a motor having a rotor with four magnetic pole pairs was exemplified, but the number of pole pairs of the rotor is not limited to four, and the number of pole pairs of the rotor may be P (P is an integer greater than or equal to 2).

[0097] Third Embodiment Next, a third embodiment of the present invention will be described. FIG. 11 is a block diagram schematically illustrating the configuration of a position estimation device 120 according to a third embodiment of the present invention. As shown in FIG. 11, the position estimation device 120 is a device that estimates the rotational position (rotation angle) of a motor 200 that includes a rotor 210 having P magnetic pole pairs (P is an integer equal to or greater than 2). In this embodiment, as an example, the rotor 210 has four magnetic pole pairs. Since the configuration of the motor 200 is the same as in the first embodiment, a description of the motor 200 in the third embodiment will be omitted.

[0098] The position estimation device 120 includes a sensor magnet 10, three fourth magnetic sensors 61, 62, and 63, three second magnetic sensors 31, 32, and 33, and a signal processing device 40. Although not shown in Fig. 11, a circuit board is attached to the motor 200, and the fourth magnetic sensors 61, 62, and 63, the second magnetic sensors 31, 32, and 33, and the signal processing device 40 are arranged on the circuit board.

[0099] The sensor magnet 10 is the same as in the first embodiment. That is, the sensor magnet 10 is a disk-shaped magnet that has one magnetic pole pair and shares a rotation axis with the rotor 210. When the rotor 210 rotates, the sensor magnet 10 rotates in synchronization with the rotor 210.

[0100] The fourth magnetic sensors 61, 62, and 63 are magnetic sensors that face the sensor magnet 10 and are arranged at predetermined intervals on the circuit board along the rotation direction of the sensor magnet 10. In this embodiment, the position estimation device 120 includes three fourth magnetic sensors 61, 62, and 63, but the number of fourth magnetic sensors may be N4 (N4 is an integer equal to or greater than 3). For example, the fourth magnetic sensors 61, 62, and 63 are each a Hall element or a linear Hall IC. The fourth magnetic sensors 61, 62, and 63 each output an analog signal whose electric signal fluctuates according to magnetic field strength. One electrical angle cycle of the analog signal output from each of the fourth magnetic sensors 61, 62, and 63 corresponds to one mechanical angle cycle.

[0101] In this embodiment, the fourth magnetic sensors 61, 62, and 63 are arranged at 120° intervals along the rotation direction of the sensor magnet 10. Therefore, the analog signals output from the fourth magnetic sensors 61, 62, and 63 have a phase difference (fourth phase difference) of 120° electrical angle from one another. Hereinafter, the analog signals output from the fourth magnetic sensors 61, 62, and 63 will be referred to as absolute analog signals. The fourth magnetic sensor 61 outputs an absolute analog signal HC1 to the signal processing device 40. The fourth magnetic sensor 62 outputs an absolute analog signal HC2 to the signal processing device 40. The fourth magnetic sensor 63 outputs an absolute analog signal HC3 to the signal processing device 40.

[0102] The second magnetic sensors 31, 32, and 33 are magnetic sensors that are arranged on the circuit board facing the rotor 210 and at predetermined intervals along the rotation direction of the rotor 210. The second magnetic sensors 31, 32, and 33 are the same as those in the first embodiment, and therefore a description of the second magnetic sensors 31, 32, and 33 in the third embodiment will be omitted.

[0103] The signal processing device 40 of the third embodiment estimates the rotational position of the motor 200, i.e., the rotational position of the rotor 210, based on the absolute analog signals HC1, HC2, and HC3 output from the fourth magnetic sensors 61, 62, and 63, and the incremental signals Hu, Hv, and Hw output from the second magnetic sensors 31, 32, and 33. The signal processing device 40 includes a processing unit 41 and a storage unit 42. The storage unit 42 is the same as in the first embodiment, and therefore a description of the storage unit 42 will be omitted in the third embodiment.

[0104] Absolute analog signals HC1, HC2, and HC3 and incremental signals Hu, Hv, and Hw are input to the processing unit 41. The absolute analog signals HC1, HC2, and HC3 and incremental signals Hu, Hv, and Hw are converted into digital signals via an A / D converter inside the processing unit 41, but for convenience of explanation, the digital signals output from the A / D converter will also be referred to as absolute analog signals HC1, HC2, and HC3 and incremental signals Hu, Hv, and Hw. Furthermore, in the following explanation, the absolute analog signals HC1, HC2, and HC3 and incremental signals Hu, Hv, and Hw input to the processing unit 41 may be collectively referred to as "input sensor signals."

[0105] The processing unit 41 executes at least the following two processes in accordance with the program stored in the storage unit 42. The processing unit 41 executes a learning process to acquire learning data necessary for estimating the rotational position of the rotor 210 based on the input sensor signal. The processing unit 41 executes a position estimation process to estimate the rotational position of the rotor 210 based on the input sensor signal and the learning data. In the third embodiment, the contents of the learning process and the position estimation process executed by the processing unit 41 differ from those in the first and second embodiments.

[0106] Next, a learning process executed by the processing unit 41 of the third embodiment will be described. The learning process corresponds to the learning step in the position estimation method of claim 3. Fig. 12 is a flowchart showing the learning process executed by the processing unit 41 in the third embodiment. The processing unit 41 executes the learning process shown in Fig. 12 at least when the power of the signal processing device 40 is turned on for the first time.

[0107] 12, when the processing unit 41 starts the learning process, first, it executes a first process of rotating the sensor magnet 10 together with the rotor 210 (step S41). This first process corresponds to the first step of the learning step in the position estimation method of claim 3.

[0108] Next, the processing unit 41 executes a second process (step S42) to acquire three absolute analog signals HC1, HC2, and HC3 via the three fourth magnetic sensors 61, 62, and 63. This second process corresponds to the second learning step in the position estimation method of claim 3.

[0109] As shown in Fig. 13, one electrical cycle of each of the absolute analog signals HC1, HC2, and HC3 corresponds to one mechanical cycle. In Fig. 13, the period from time t1 to time t9 corresponds to one mechanical cycle. The absolute analog signals HC1, HC2, and HC3 have a phase difference of 120° electrical angle from one another.

[0110] Subsequently, the processing unit 41 executes a third process (step S43) to acquire three incremental signals Hu, Hv, and Hw via the three second magnetic sensors 31, 32, and 33. This third process corresponds to the third step of the learning step in the position estimation method of claim 3.

[0111] As shown in Fig. 13, similar to the first embodiment, one electrical angle cycle of each of the incremental signals Hu, Hv, and Hw corresponds to ¼ of one mechanical angle cycle, i.e., 90° mechanical angle. In Fig. 13, the periods from time t1 to time t3, from time t3 to time t5, from time t5 to time t7, and from time t7 to time t9 each correspond to 90° mechanical angle. Furthermore, the incremental signals Hu, Hv, and Hw have a phase difference of 120° electrical angle from one another.

[0112] Next, the processing unit 41 executes a fourth process of dividing the learning period into a plurality of quadrants based on the three absolute analog signals HC1, HC2, and HC3 obtained during the learning period corresponding to one mechanical angle cycle (step S44). This fourth process corresponds to the fourth step of the learning step in the position estimation method of claim 3.

[0113] In step S44, the processing unit 41 executes a process of extracting zero-crossing points, which are points where the three absolute analog signals HC1, HC2, and HC3 cross a reference value of "0." As shown in Fig. 13, the processing unit 41 extracts points P20, P21, P22, P23, P24, P25, and P26 as the zero-crossing points of the absolute analog signals HC1, HC2, and HC3. Then, the processing unit 41 divides the section between two adjacent zero-crossing points into quadrants.

[0114] As shown in FIG. 13, the processing unit 41 divides the section between the zero cross point P20 and the zero cross point P21 into the first quadrant. The processing unit 41 divides the section between the zero cross point P21 and the zero cross point P22 into a second quadrant. The processing unit 41 divides the section between the zero cross point P22 and the zero cross point P23 into the third quadrant. The processing unit 41 divides the section between the zero cross point P23 and the zero cross point P24 into the fourth quadrant. The processing unit 41 divides the section between the zero cross point P24 and the zero cross point P25 into the fifth quadrant. The processing unit 41 divides the section between the zero cross point P25 and the zero cross point P26 into the sixth quadrant. In this way, in the third embodiment, the processing unit 41 divides the learning period into six quadrants based on the absolute analog signals HC1, HC2, and HC3.

[0115] Next, the processing unit 41 executes a fifth process (step S45) in which, based on the three incremental signals Hu, Hv, and Hw obtained during the learning period, the learning period is divided into four pole pair regions associated with pole pair numbers representing the pole pair positions of the four magnetic pole pairs, each of the four pole pair regions is further divided into a plurality of sections, and a segment number representing the rotational position of the rotor 210 is associated with each of the plurality of sections. This fifth process corresponds to the fifth step of the learning step in the position estimation method of claim 3.

[0116] By performing the processing of step S45, the learning period is divided into four pole pair regions linked to pole pair numbers, each of the four pole pair regions is divided into 12 sections, and each section is linked to a segment number, as shown in Fig. 13. The processing of step S45 is similar to step S5 of the learning processing in the first embodiment, so a description of step S45 in the third embodiment will be omitted.

[0117] Next, the processing unit 41 executes a sixth process of acquiring data indicating the correspondence between the segment numbers associated with the sections included in each of the six quadrants and the pole pair numbers indicating the pole pair positions as learning data, and storing the acquired learning data in the storage unit 42 (step S46). This sixth process corresponds to the sixth step of the learning step in the position estimation method of claim 3. As shown in Fig. 13, the learning data acquired in the third embodiment is similar to the learning data acquired in the first embodiment, and therefore, a description of the learning data in the third embodiment will be omitted.

[0118] Next, a position estimation process executed by the processing unit 41 of the third embodiment will be described. The position estimation process of the third embodiment corresponds to the position estimation step in the position estimation method of claim 3. Fig. 14 is a flowchart showing the position estimation process executed by the processing unit 41 in the third embodiment. After executing the learning process shown in Fig. 12, the processing unit 41 executes the position estimation process shown in Fig. 14 when the power of the signal processing device 40 is turned on again.

[0119] 14, when the processing unit 41 starts the position estimation process, first, it executes a seventh process (step S47) to acquire three absolute analog signals HC1, HC2, and HC3 via the three fourth magnetic sensors 61, 62, and 63. This seventh process corresponds to the seventh step of the position estimation step in the position estimation method of claim 3.

[0120] Subsequently, the processing unit 41 executes an eighth process (step S48) to acquire three incremental signals Hu, Hv, and Hw via the three second magnetic sensors 31, 32, and 33. This eighth process corresponds to the eighth step of the position estimation step in the position estimation method of claim 3.

[0121] Next, the processing unit 41 executes a ninth process (step S49) to identify the current quadrant from among the six quadrants based on the three absolute analog signals HC1, HC2, and HC3 acquired in the seventh process (step S47). This ninth process corresponds to the ninth step of the position estimation step in the position estimation method of claim 3.

[0122] In step S49, the processing unit 41 identifies the current quadrant from among the six quadrants, for example, based on the magnitude relationship between the detected values ​​of the absolute analog signals HC1, HC2, and HC3, the positive or negative sign of each detected value, etc. For example, it is assumed that the processing unit 41 identifies the second quadrant from among the six quadrants as the current quadrant.

[0123] Next, the processing unit 41 executes a tenth process (step S50) to identify the current section from among the 12 sections based on the three incremental signals Hu, Hv, and Hw acquired in the eighth process (step S48). This tenth process corresponds to the tenth step of the position estimation step in the position estimation method of claim 3. Since the method for identifying the current section is the same as in the first embodiment, a description of the method for identifying the current section in the third embodiment will be omitted. For example, it is assumed that the processing unit 41 identifies section 2 as the current section.

[0124] Next, the processing unit 41 executes an eleventh process (step S51) to determine, based on the learning data stored in the storage unit 42, the pole pair number corresponding to the segment number associated with the current section included in the current quadrant as the initial position of the rotor 210. This eleventh process corresponds to the eleventh step of the position estimation step in the position estimation method of claim 3.

[0125] For example, assume that the second quadrant is identified as the current quadrant and the second section is identified as the current section, as described above. As shown in Fig. 13, the learning data includes data indicating the correspondence between segment numbers "12" to "15" associated with sections 0 to 3 included in the second quadrant and pole pair number "1." Therefore, when the second quadrant is identified as the current quadrant and section 2 is identified as the current section, processing unit 41 determines, as the initial position of rotor 210, pole pair number "1" corresponding to segment number "14" associated with section 2 included in the second quadrant.

[0126] As described above, the position estimation device 120 of the third embodiment includes a processing unit 41 that executes a learning process to acquire learning data necessary for estimating the rotational position of the rotor 210 based on input sensor signals, and a position estimation process to estimate the rotational position of the rotor 210 based on the input sensor signals and the learning data. The processing unit 41 executes the learning process at least when the signal processing device 40 is powered on for the first time, thereby acquiring data indicating the correspondence between segment numbers associated with sections included in each of the six quadrants and pole pair numbers indicating pole pair positions as learning data. The processing unit 41 executes the position estimation process when the signal processing device 40 is powered on again, thereby determining the initial position of the rotor 210.

[0127] As a result, similar to the first embodiment, the position estimation device 120 of the third embodiment can estimate the initial position of the rotor 210 without rotating the rotor 210. Therefore, the motor 200 equipped with the position estimation device 120 does not need to adjust the origin of the rotational position of the rotor 210 when powered on. Because the motor 200 does not require a preliminary rotation operation for adjusting the origin, it can also be suitably used as a drive motor for robots, automatic guided vehicles, and the like, which are not permitted to perform preliminary rotation operation. Because the motor 200 does not require a preliminary rotation operation for adjusting the origin, the drive time and power consumption required for the preliminary rotation operation can be reduced.

[0128] (Modification of the third embodiment) The present invention is not limited to the third embodiment, and the configurations described in this specification can be combined as appropriate within a range that does not contradict each other. In the third embodiment described above, the processing unit 41 divides the learning period into six quadrants by extracting the zero-crossing points of the three absolute analog signals HC1, HC2, and HC3 obtained during the learning period corresponding to one mechanical angle cycle. For example, as shown in Fig. 15, the processing unit 41 may divide the learning period into six quadrants by extracting the intersection points of the three absolute analog signals HC1, HC2, and HC3 obtained during the learning period corresponding to one mechanical angle cycle.

[0129] Specifically, in the modification shown in Fig. 15, the processing unit 41 executes a process of extracting intersections where three absolute analog signals HC1, HC2, and HC3 intersect with one another. As shown in Fig. 15, the processing unit 41 extracts points P27, P28, P29, P30, P31, and P32 as the intersections of the absolute analog signals HC1, HC2, and HC3. The processing unit 41 then divides the section between two adjacent intersections into quadrants.

[0130] As shown in FIG. 15, the processing unit 41 divides the section between intersection point P32 and intersection point P27 into the first quadrant. The processing unit 41 divides the section between intersection point P27 and intersection point P28 into the second quadrant. The processing unit 41 divides the section between intersection point P28 and intersection point P29 into the third quadrant. The processing unit 41 divides the section between intersection point P29 and intersection point P30 into the fourth quadrant. The processing unit 41 divides the section between intersection point P30 and intersection point P31 into the fifth quadrant. The processing unit 41 divides the section between intersection point P31 and intersection point P32 into the sixth quadrant.

[0131] Furthermore, for example, as shown in Fig. 16, the processing unit 41 may extract zero crossing points and intersection points of three absolute analog signals HC1, HC2, and HC3 obtained during a learning period corresponding to one mechanical angle cycle, thereby dividing the learning period into 12 quadrants. Specifically, in the modification shown in Fig. 16, the processing unit 41 divides the section between adjacent zero crossing points and intersection points into quadrants.

[0132] As shown in FIG. 16, the processing unit 41 divides the section between the zero cross point P20 and the intersection point P27 into the first quadrant. The processing unit 41 divides the section between the intersection point P27 and the zero cross point P21 into the second quadrant. The processing unit 41 divides the section between the zero cross point P21 and the intersection point P28 into the third quadrant. The processing unit 41 divides the section between the intersection point P28 and the zero cross point P22 into the fourth quadrant. The processing unit 41 divides the section between the zero cross point P22 and the intersection point P29 into the fifth quadrant. The processing unit 41 divides the section between the intersection point P29 and the zero cross point P23 into the sixth quadrant.

[0133] The processing unit 41 divides the section between zero cross point P23 and intersection point P30 into the seventh quadrant. The processing unit 41 divides the section between intersection point P30 and zero cross point P24 into the eighth quadrant. The processing unit 41 divides the section between zero cross point P24 and intersection point P31 into the ninth quadrant. The processing unit 41 divides the section between intersection point P31 and zero cross point P25 into the tenth quadrant. The processing unit 41 divides the section between zero cross point P25 and intersection point P32 into the eleventh quadrant. The processing unit 41 divides the section between intersection point P32 and zero cross point P26 into the twelfth quadrant. In this way, by extracting the zero crossing points and intersection points of the three absolute analog signals HC1, HC2, and HC3 obtained during a learning period corresponding to one mechanical angle cycle, the learning period can be divided into 12 quadrants.

[0134] Furthermore, in the third embodiment, an example has been given in which three fourth magnetic sensors that output absolute analog signals are provided, but the number of fourth magnetic sensors is not limited to three, and may be N4 (N4 is an integer equal to or greater than 3) as long as the number of fourth magnetic sensors is N4. In other words, the number of fourth magnetic sensors may be four or more. In addition, in the above third embodiment, an example was given of a case where three second magnetic sensors that output incremental signals are provided, but the number of second magnetic sensors is not limited to three, and the number of second magnetic sensors may be N2 (N2 is an integer greater than or equal to 3). In addition, in the above third embodiment, a motor having a rotor with four magnetic pole pairs was exemplified, but the number of pole pairs of the rotor is not limited to four, and the number of pole pairs of the rotor may be P (P is an integer greater than or equal to 2).

[0135] [Application example] FIG. 17 is a diagram showing the appearance of an automatic guided vehicle 300 to which the present invention is applied. FIG. 18 is a diagram showing the appearance of a sewing device 400 to which the present invention is applied. The automated guided vehicle 300 and the sewing device 400 are each equipped with a motor having a rotor with P (P is an integer of 2 or more) magnetic pole pairs, and a position estimation device that estimates the rotational position of the motor. The motor may be the motor 200 described in the above embodiment. The position estimation device may be any of the position estimation devices 100 of the first embodiment, 110 of the second embodiment, and 120 of the third embodiment. The motors provided in such an automatic guided vehicle 300 and sewing device 400 do not require a preliminary rotation operation for adjusting the origin, so that unintended operation of the automatic guided vehicle 300 and sewing device 400 can be prevented. The application of the present invention is not limited to the automated guided vehicle 300 and the sewing device 400, but can be widely applied to devices that cannot tolerate preliminary rotation of the motor, such as robots. [Explanation of symbols]

[0136] 100, 110, 120...position estimation device, 10...sensor magnet (magnet), 21, 22, 23...first magnetic sensor, 31, 32, 33...second magnetic sensor, 40...signal processing device, 41...processing unit, 42...storage unit, 51, 52...third magnetic sensor, 61, 62, 63...fourth magnetic sensor, 200...motor, 210...rotor, 300...automated guided vehicle, 400...sewing device

Claims

1. 1. A method for estimating a rotational position of a motor having a rotor with P magnetic pole pairs (P is an integer equal to or greater than 2), comprising: a learning step of acquiring learning data necessary for estimating the rotational position; a position estimating step of estimating a rotational position of the rotor based on the learning data; and The learning step includes: a first step of rotating a magnet having one magnetic pole pair and sharing a rotation axis with the rotor together with the rotor; a second step of acquiring N1 digital signals whose levels are inverted every time the magnet rotates 180° and whose phases are different from one another, using N1 (N1 is an integer of 3 or more) first magnetic sensors that face the magnet and are arranged along the direction of rotation of the magnet; and a third step of acquiring N2 analog signals whose electric signals vary according to magnetic field strength and whose phases are different from one another, using N2 (N2 is an integer of 3 or more) second magnetic sensors that face the rotor and are arranged along the direction of rotation of the rotor. a fourth step of dividing the learning period into a plurality of quadrants each having a different N1-bit digital value based on the N1 digital signals obtained during the learning period corresponding to one period in mechanical angle; a fifth step of dividing the learning period into P pole pair regions associated with pole pair numbers representing respective pole pair positions of the P magnetic pole pairs based on the N2 analog signals obtained during the learning period, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections; a sixth step of acquiring, as the learning data, data indicating a correspondence relationship between the segment numbers associated with the sections included in each of the plurality of quadrants and the pole pair numbers indicating the pole pair positions; and The position estimation step includes: a seventh step of acquiring the N1 digital signals using the N1 first magnetic sensors; an eighth step of acquiring the N2 analog signals using the N2 second magnetic sensors; a ninth step of identifying a current quadrant from among the plurality of quadrants based on the N1 digital signals acquired in the seventh step; a tenth step of identifying a current section from among the plurality of sections based on the N2 analog signals acquired in the eighth step; an eleventh step of determining, based on the learning data, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant as an initial position of the rotor; having Location estimation method.

2. 1. A method for estimating a rotational position of a motor having a rotor with P magnetic pole pairs (P is an integer equal to or greater than 2), comprising: a learning step of acquiring learning data necessary for estimating the rotational position; a position estimating step of estimating a rotational position of the rotor based on the learning data; and The learning step includes: a first step of rotating a magnet having one magnetic pole pair and sharing a rotation axis with the rotor together with the rotor; a second step of acquiring N3 analog signals, the electrical signals of which vary according to magnetic field strength and have a third phase difference from one another, using N3 (N3 is an integer of 2 or more) third magnetic sensors that face the magnet and are arranged along the rotation direction of the magnet; a third step of acquiring N2 analog signals, the electrical signals of which vary according to magnetic field strength and have a second phase difference from one another, using N2 second magnetic sensors (N2 is an integer equal to or greater than 3) that face the rotor and are arranged along the rotation direction of the rotor; a fourth step of calculating time series data of the mechanical angle during a learning period based on the N3 analog signals obtained during the learning period, the time series data corresponding to one period of the mechanical angle; a fifth step of dividing the learning period into P pole pair regions associated with pole pair numbers representing respective pole pair positions of the P magnetic pole pairs based on the N2 analog signals obtained during the learning period, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections; a sixth step of acquiring, as the learning data, data indicating a correspondence relationship between the time-series data of the mechanical angle and the pole pair number; and The position estimation step includes: a seventh step of acquiring the N3 analog signals using the N3 third magnetic sensors; an eighth step of calculating a current value of the mechanical angle based on the N3 analog signals acquired in the seventh step; a ninth step of determining, based on the learning data, a pole pair number corresponding to the current value of the mechanical angle as an initial position of the rotor; having Location estimation method.

3. 1. A method for estimating a rotational position of a motor having a rotor with P magnetic pole pairs (P is an integer equal to or greater than 2), comprising: a learning step of acquiring learning data necessary for estimating the rotational position; a position estimating step of estimating a rotational position of the rotor based on the learning data; and The learning step includes: a first step of rotating a magnet having one magnetic pole pair and sharing a rotation axis with the rotor together with the rotor; a second step of acquiring N4 analog signals, the electrical signals of which vary according to magnetic field strength and have a fourth phase difference from one another, using N4 (N4 is an integer of 3 or more) fourth magnetic sensors that face the magnet and are arranged along the rotation direction of the magnet; a third step of acquiring N2 analog signals, the electrical signals of which vary according to magnetic field strength and have a second phase difference from one another, using N2 second magnetic sensors (N2 is an integer equal to or greater than 3) that face the rotor and are arranged along the rotation direction of the rotor; a fourth step of dividing the learning period into a plurality of quadrants based on the N4 analog signals obtained during the learning period corresponding to one period in mechanical angle; a fifth step of dividing the learning period into P pole pair regions associated with pole pair numbers representing respective pole pair positions of the P magnetic pole pairs based on the N2 analog signals obtained during the learning period, further dividing each of the P pole pair regions into a plurality of sections, and associating a segment number representing the rotational position with each of the plurality of sections; a sixth step of acquiring, as the learning data, data indicating a correspondence relationship between the segment numbers associated with the sections included in each of the plurality of quadrants and the pole pair numbers indicating the pole pair positions; and The position estimation step includes: a seventh step of acquiring the N4 analog signals using the N4 fourth magnetic sensors; an eighth step of acquiring the N2 analog signals using the N2 second magnetic sensors; a ninth step of identifying a current quadrant from among the plurality of quadrants based on the N4 analog signals acquired in the seventh step; a tenth step of identifying a current section from among the plurality of sections based on the N2 analog signals acquired in the eighth step; an eleventh step of determining, based on the learning data, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant as an initial position of the rotor; having Location estimation method.

4. The fifth step of the learning step is extracting zero-crossing points, which are points at which the N2 analog signals included in each of the P pole pair regions cross a reference value; extracting intersections where the N2 analog signals included in each of the P pole pair regions intersect with each other; determining an interval between the zero crossing point and the intersection point adjacent to each other as the section; having The position estimation method according to any one of claims 1 to 3.

5. the learning step is performed when a power source of a signal processing device that executes at least the processing according to the learning step and the position estimation step is turned on for the first time; the position estimation step is performed when the power supply of the signal processing device is turned on again after the learning step is executed. The position estimation method according to any one of claims 1 to 4.

6. An apparatus for estimating a rotational position of a motor having a rotor with P magnetic pole pairs (P is an integer of 2 or more), comprising: a magnet having one magnetic pole pair and sharing a rotation axis with the rotor; N1 (N1 is an integer of 3 or more) first magnetic sensors facing the magnet and arranged along the rotation direction of the magnet; N2 (N2 is an integer of 3 or more) second magnetic sensors facing the rotor and arranged along the rotation direction of the rotor; a signal processing device that processes output signals from the first magnetic sensor and the second magnetic sensor; Equipped with The signal processing device includes: a processing unit that executes a learning process to acquire learning data necessary for estimating the rotational position, and a position estimation process to estimate the rotational position of the rotor based on the learning data; a storage unit that stores the learning data; and The processing unit performs the learning process by: a first process of rotating the magnet together with the rotor; a second process of acquiring, via the N1 first magnetic sensors, N1 digital signals whose levels are inverted every time the magnet rotates 180° and which have a first phase difference from one another; and a third process of acquiring, via the N2 second magnetic sensors, N2 analog signals whose electric signals fluctuate according to magnetic field strength and which have a second phase difference from one another. a fourth process of dividing the learning period into a plurality of quadrants each having a different N1-bit digital value based on the N1 digital signals obtained during the learning period corresponding to one period in mechanical angle; a fifth process for dividing the learning period into P pole pair regions linked to pole pair numbers representing pole pair positions of the P magnetic pole pairs based on the N2 analog signals obtained during the learning period, further dividing each of the P pole pair regions into a plurality of sections, and linking each of the plurality of sections with a segment number representing the rotation position; and a sixth process for storing data indicating a correspondence relationship between the segment numbers linked to the sections included in each of the plurality of quadrants and the pole pair numbers representing the pole pair positions as the learning data in the storage unit. Run The processing unit performs the position estimation process by: a seventh process of acquiring the N1 digital signals via the N1 first magnetic sensors; an eighth process of acquiring the N2 analog signals via the N2 second magnetic sensors; a ninth process of identifying a current quadrant from among the plurality of quadrants based on the N1 digital signals acquired in the seventh process; a tenth process of identifying a current section from among the plurality of sections based on the N2 analog signals acquired in the eighth process; an eleventh process of determining, based on the learning data, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant as an initial position of the rotor; To execute Location estimation device.

7. An apparatus for estimating a rotational position of a motor having a rotor with P magnetic pole pairs (P is an integer of 2 or more), comprising: a magnet having one magnetic pole pair and sharing a rotation axis with the rotor; N3 (N3 is an integer of 2 or more) third magnetic sensors facing the magnet and arranged along the rotation direction of the magnet; N2 (N2 is an integer of 3 or more) second magnetic sensors facing the rotor and arranged along the rotation direction of the rotor; a signal processing device that processes output signals from the second magnetic sensor and the third magnetic sensor; Equipped with The signal processing device includes: a processing unit that executes a learning process to acquire learning data necessary for estimating the rotational position, and a position estimation process to estimate the rotational position of the rotor based on the learning data; a storage unit that stores the learning data; and The processing unit performs the learning process by: a first process of rotating the magnet together with the rotor; a second process of acquiring, via the N3 third magnetic sensors, N3 analog signals whose electric signals fluctuate according to magnetic field strength and have a third phase difference from each other; a third process of acquiring, via the N2 second magnetic sensors, N2 analog signals whose electric signals fluctuate according to magnetic field strength and have a second phase difference from each other; a fourth process of calculating time series data of the mechanical angle during a learning period based on the N3 analog signals obtained during the learning period, the N3 analog signals corresponding to one period of the mechanical angle; a fifth process for dividing the learning period into P pole pair regions linked to pole pair numbers representing pole pair positions of the P magnetic pole pairs based on the N2 analog signals obtained during the learning period, further dividing each of the P pole pair regions into a plurality of sections, and linking each of the plurality of sections to a segment number representing the rotation position; and a sixth process for storing data representing a correspondence relationship between the time series data of the mechanical angle and the pole pair numbers as the learning data in the storage unit. Run The processing unit performs the position estimation process by: a seventh process of acquiring the N3 analog signals via the N3 third magnetic sensors; an eighth process of calculating a current value of the mechanical angle based on the N3 analog signals acquired in the seventh process; a ninth process of determining, as an initial position of the rotor, a pole pair number corresponding to the current value of the mechanical angle based on the learning data stored in the storage unit; To execute Location estimation device.

8. An apparatus for estimating a rotational position of a motor having a rotor with P magnetic pole pairs (P is an integer of 2 or more), comprising: a magnet having one magnetic pole pair and sharing a rotation axis with the rotor; N4 (N4 is an integer of 3 or more) fourth magnetic sensors facing the magnet and arranged along the rotation direction of the magnet; N2 (N2 is an integer of 3 or more) second magnetic sensors facing the rotor and arranged along the rotation direction of the rotor; a signal processing device that processes output signals from the second magnetic sensor and the fourth magnetic sensor; Equipped with The signal processing device includes: a processing unit that executes a learning process to acquire learning data necessary for estimating the rotational position, and a position estimation process to estimate the rotational position of the rotor based on the learning data; a storage unit that stores the learning data; and The processing unit performs the learning process by: a first process of rotating the magnet together with the rotor; a second process of acquiring, via the N4 fourth magnetic sensors, N4 analog signals whose electric signals fluctuate according to magnetic field strength and have a fourth phase difference from each other; a third process of acquiring, via the N2 second magnetic sensors, N2 analog signals whose electric signals fluctuate according to magnetic field strength and have a second phase difference from each other; a fourth process of dividing the learning period into a plurality of quadrants based on the N4 analog signals obtained during the learning period corresponding to one period in mechanical angle; a fifth process for dividing the learning period into P pole pair regions linked to pole pair numbers representing pole pair positions of the P magnetic pole pairs based on the N2 analog signals obtained during the learning period, further dividing each of the P pole pair regions into a plurality of sections, and linking each of the plurality of sections with a segment number representing the rotation position; and a sixth process for storing data indicating a correspondence relationship between the segment numbers linked to the sections included in each of the plurality of quadrants and the pole pair numbers representing the pole pair positions as the learning data in the storage unit. Run The processing unit performs the position estimation process by: a seventh process of acquiring the N4 analog signals via the N4 fourth magnetic sensors; an eighth process of acquiring the N2 analog signals via the N2 second magnetic sensors; a ninth process of identifying a current quadrant from among the plurality of quadrants based on the N4 analog signals acquired in the seventh process; a tenth process of identifying a current section from among the plurality of sections based on the N2 analog signals acquired in the eighth process; an eleventh process of determining, based on the learning data, a pole pair number corresponding to a segment number associated with the current section included in the current quadrant as an initial position of the rotor; To execute Location estimation device.

9. In the fifth process of the learning process, the processing unit A process of extracting zero crossing points, which are points at which the N2 analog signals included in each of the P pole pair regions cross a reference value; A process of extracting intersections where the N2 analog signals included in each of the P pole pair regions intersect with each other; a process of determining an interval between the zero crossing point and the intersection point adjacent to each other as the section; To execute The position estimation device according to any one of claims 6 to 8.

10. the processing unit executes the learning process at least when the signal processing device is powered on for the first time; the processing unit executes the position estimation process when the power supply of the signal processing device is turned on again after executing the learning process. The position estimation device according to any one of claims 6 to 9.

11. a motor including a rotor having P magnetic pole pairs (P is an integer of 2 or more); a position estimation device according to any one of claims 6 to 10, which estimates a rotational position of the motor; An unmanned guided vehicle equipped with

12. a motor including a rotor having P magnetic pole pairs (P is an integer of 2 or more); a position estimation device according to any one of claims 6 to 10, which estimates a rotational position of the motor; A sewing device comprising:

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