Motor rotor position measurement accuracy compensation method, device and storage medium

Abstract

The present disclosure provides a method, device, and storage medium for compensating for the position measurement accuracy of a motor rotor. The method includes: constructing a motor rotor position measurement accuracy compensation neural network, wherein the input of the network is the error term-related variables of various influencing factors obtained based on the motor rotor position estimate and the motor q-axis current, and the output is the motor rotor position estimation error; using a Hall sensor to obtain the motor rotor position observation value, and collecting the actual position and command position of the motor rotor to form a training set; using the training set to train the motor rotor position measurement accuracy compensation neural network; inputting the error term-related variables obtained based on the motor rotor position observation value and the motor q-axis current into the trained motor rotor position measurement accuracy compensation neural network, outputting the motor rotor position estimation error, and thereby performing error compensation on the motor rotor position observation value. The present disclosure can effectively reduce the motor rotor position estimation error and improve the robustness of the motor.

Description

Technical Field

[0001] The present disclosure relates to the field of permanent magnet synchronous motor control, and in particular to a method, device, and storage medium for compensating for motor rotor position measurement accuracy. Background Art

[0002] Vector control is a commonly used control method for permanent magnet synchronous linear motors. To achieve this control method, it is necessary to ensure real-time measurement of the motor rotor position to adjust its drive current. In industry, linear Hall sensors are widely used to measure the motor rotor position.

[0003] Permanent magnet synchronous linear motors have the characteristics of simple structure, high thrust density, good dynamic response performance and no lateral end effect, which makes them have broad application prospects in the field of linear drives.

[0004] The current accuracy compensation method for measuring the position of a motor's rotor using a linear Hall sensor results in large measurement errors when the motor is used in complex working environments and conditions, and cannot meet the requirements of the servo system. Summary of the Invention

[0005] The present disclosure aims to solve one of the above-mentioned problems.

[0006] To this end, the first aspect of the present disclosure provides a method for compensating the position measurement accuracy of a motor mover, which can effectively reduce the position estimation error of the motor mover to improve the robustness of the motor, including:

[0007] Constructing a motor mover position measurement accuracy compensation neural network, wherein the input of the motor mover position measurement accuracy compensation neural network is the error term related variables of various influencing factors obtained according to the motor mover position estimation value and the motor q-axis current, and the output is the motor mover position estimation error;

[0008] The Hall effect sensor is used to obtain the observed value of the motor rotor position, and the actual position and command position of the motor rotor are collected to form a training set;

[0009] Using the training set to train the motor mover position measurement accuracy compensation neural network to obtain a trained motor mover position measurement accuracy compensation neural network;

[0010] The variables related to the error terms obtained based on the motor rotor position observation value and the motor q-axis current are input into the trained motor rotor position measurement accuracy compensation neural network, and the motor rotor position estimation error is output to compensate the motor rotor position observation value for the error.

[0011] The motor mover position measurement accuracy compensation method provided by the first embodiment of the present disclosure has the following characteristics and beneficial effects:

[0012] The motor rotor position measurement accuracy compensation method provided by the embodiment of the first aspect of the present disclosure takes into account the influence of various interference factors such as motor temperature, winding current, harmonic influence, etc. on the position accuracy of the Hall sensor, can effectively reduce the estimation error of the motor rotor position measured by the Hall sensor, has higher robustness to the motor working environment and working conditions, and has a small amount of calculation, and is suitable for construction and implementation in DSP (Digital Signal Processing, digital signal processor).

[0013] In some embodiments, the motor mover position measurement accuracy compensation method is applied to the case where the position of a cylindrical permanent magnet synchronous linear motor mover is measured using two Hall sensors spaced 90 degrees apart in electrical angle. The motor mover position measurement accuracy compensation neural network constructed uses an Adaline neural network, and the following formula is satisfied between its input and output:

[0014] y=w T x

[0015] x=[i q sinθ cosθ sin2θ cos2θ sin4θ sin8θ] T

[0016] w=[w0 w1 w2 w3 w4 w5 w6] T

[0017]

[0018] Among them, x is the input of the motor rotor position measurement accuracy compensation neural network, which is a vector whose elements represent the relevant variables composed of q-axis current and motor rotor estimated position sine and cosine in the error terms caused by the motor winding excitation magnetic field, Hall sensor zero drift, magnetic field harmonics, Hall sensor radial installation error and amplification factor difference, Hall sensor axial installation error, and uneven magnetization intensity of the motor permanent magnet, and θ is the motor rotor position observation value; w is the weight vector composed of the weight coefficients of the motor rotor position measurement accuracy compensation neural network, and each weight coefficient w0~w6 represents the coefficient of the relevant variable of each error term; y is the output of the motor rotor position measurement accuracy compensation neural network, that is, the motor rotor position estimation error

[0019] In some embodiments, the motor position estimation error Expressed as:

[0020]

[0021] in:

[0022] e d=λb0sinθ-a0cosθ e c =-h0i q

[0023]

[0024]

[0025]

[0026] Where θ is the motor rotor position observation value obtained by two Hall sensors; a0 and b0 are the zero drift values ​​of the two Hall sensors, a n and b n are the 2n-1th harmonic amplitudes of the magnetic field of the two Hall sensors respectively; e d The error caused by the zero drift of the Hall sensor; e c is the error term generated by the excitation magnetic field of the motor winding; e h is the error term generated by the magnetic field harmonics; e r The error term caused by the radial installation error of the Hall sensor and the difference in the amplification factor; e a The error term caused by the axial installation error of the Hall sensor; e f is the non-uniform error term of the permanent magnet magnetization intensity of the motor, where p nj With q nj is the amplitude of the fractional harmonics of the two Hall signal errors, and the amplitude of the fractional harmonics of the Hall signal f nj , the Hall sensor axial installation error θ0 and the Hall sensor amplification coefficient difference λ are jointly determined, i q is the motor q-axis current, and h0 is the magnetic field harmonic amplitude generated by unit current.

[0027] In some embodiments, each error term is simplified as follows:

[0028] The error term e generated by the magnetic field harmonics h , only consider the influence of n+1=3, 5, and 7 harmonics, where the error expressions of the 3rd and 5th harmonics are the 4th harmonic functions of the motor rotor position, and the 7th harmonic produces an 8th harmonic error related to the motor rotor position; for the error term e caused by the radial installation error and the difference in the amplification factor of the Hall sensor r and the error term e caused by the axial installation error of the Hall sensor a , only the error caused by the fundamental wave in the expression is compensated; the uneven error of the magnetization intensity of the permanent magnet of the motor e is ignored f Impact on the measurement accuracy of the motor rotor position;

[0029] Based on the above simplification, the expressions of each element in the weight vector w are as follows:

[0030] w0=-h0

[0031] w1=λb0

[0032] w2=-a0

[0033]

[0034]

[0035]

[0036]

[0037] Wherein, a1 and b1 are the 1st harmonic amplitudes of the magnetic field density of the two Hall sensors, and a2 and b2 are the 3rd harmonic amplitudes of the magnetic field density of the two Hall sensors.

[0038] In some embodiments, data points are uniformly sampled during one motion cycle of the motor mover as a training set for the Adaline network.

[0039] In some embodiments, the training objective of the motor mover position measurement accuracy compensation neural network is to minimize the sum of squares of errors between the motor mover estimation error output by the neural network and the actual position error value.

[0040] In some embodiments, the motor mover position measurement accuracy compensation method further includes: using a Romberg observer to correct the motor mover position observation value obtained by the Hall sensor.

[0041] The second aspect of the present disclosure provides a motor mover position measurement accuracy compensation device, comprising:

[0042] A construction module is used to construct a motor mover position measurement accuracy compensation neural network, wherein the input of the motor mover position measurement accuracy compensation neural network is the error term related variables of various influencing factors obtained according to the motor mover position estimation value and the motor q-axis current, and the output is the motor mover position estimation error;

[0043] The acquisition module is used to obtain the motor mover position observation value through the Hall sensor, collect the actual position and command position of the motor mover, and together form the training set;

[0044] A training module, configured to train the motor mover position measurement accuracy compensation neural network using the training set to obtain a trained motor mover position measurement accuracy compensation neural network;

[0045] The compensation module is used to input the variables related to the error terms obtained based on the position observation value of the motor rotor and the motor q-axis current into the trained motor rotor position measurement accuracy compensation neural network, and output the motor rotor position estimation error, thereby compensating the error of the motor rotor position observation value.

[0046] The computer storage medium provided in the embodiment of the third aspect of the present disclosure stores computer instructions, and the computer instructions are used to enable the computer to execute the motor mover position measurement accuracy compensation method provided in the embodiment of the first aspect of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A flow chart of a method for compensating for motor rotor position measurement accuracy provided in an embodiment of the first aspect of the present disclosure.

[0048] Figure 2 (a1) to (d1) and (a2) to (d2) are curves showing the change over time of the motor mover command position and position estimation error obtained by using the compensation method provided by the embodiment of the first aspect of the present disclosure and without using the compensation method under different experimental conditions.

[0049] Figure 3 It is a structural diagram of the motor mover position measurement accuracy compensation device provided in the embodiment of the second aspect of the present disclosure.

[0050] Figure 4 It is a structural diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of this application more clearly understood, this application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0052] On the contrary, this application covers any alternatives, modifications, equivalents, and solutions made within the spirit and scope of this application as defined by the claims. Furthermore, to facilitate a better understanding of this application, certain specific details are described in detail below in the detailed description of this application. Those skilled in the art will be able to fully understand this application without these details.

[0053] See also Figure 1 The first aspect of the present disclosure provides a method for compensating for the position measurement accuracy of a motor rotor. The method of the embodiment of the present disclosure includes:

[0054] A motor rotor position measurement accuracy compensation neural network is constructed. Its input is the error term-related variables of various influencing factors obtained based on the motor rotor position estimation value and the motor q-axis current, and its output is the motor rotor position estimation error.

[0055] The Hall effect sensor is used to obtain the observed value of the motor rotor position, and the actual position and command position of the motor rotor are collected to form a training set;

[0056] The motor mover position measurement accuracy compensation neural network is trained using the training set to obtain a trained motor mover position measurement accuracy compensation neural network;

[0057] In the application stage, the variables related to the error terms obtained based on the motor rotor position observation value and the motor q-axis current are input into the trained motor rotor position measurement accuracy compensation neural network, and the motor rotor position estimation error is output to compensate the error of the motor rotor position observation value.

[0058] In some examples, this compensation method is applied to the case where the position of a cylindrical permanent magnet synchronous linear motor rotor is measured using two Hall sensors spaced 90 degrees apart in electrical angle. The motor rotor position measurement accuracy compensation neural network constructed uses an Adaline neural network (adaptive linear neural network). The input and output of the motor rotor position measurement accuracy compensation neural network satisfy the following formula:

[0059] y=w T x

[0060] x=[i q sinθ cosθ sin2θ cos2θ sin4θ sin8θ] T

[0061] w=[w0 w1 w2 w3 w4 w5 w6] T

[0062]

[0063] Among them, x is the input of the motor rotor position measurement accuracy compensation neural network, which is a vector whose elements represent the motor winding excitation magnetic field, Hall sensor zero drift, magnetic field harmonics, Hall sensor radial installation error and amplification factor difference, Hall sensor axial installation error, and the error term caused by the uneven magnetization intensity of the motor permanent magnet, which is composed of the q-axis current and the motor rotor estimated position sine and cosine related variables; w is the weight vector composed of the weight coefficients of the motor rotor position measurement accuracy compensation neural network, and each weight coefficient w0~w6 represents the coefficient of the related variable of each error term respectively; y is the output of the motor rotor position measurement accuracy compensation neural network, that is, the motor rotor position estimation error

[0064] Furthermore, under the premise that each influencing factor is sufficiently small, the position estimation error of the motor mover can be expressed as the sum of the motor mover position errors generated by each influencing factor, as shown in the following expression:

[0065]

[0066] in:

[0067] e d =λb0sinθ-a0cosθ e c =-h0i q

[0068]

[0069]

[0070]

[0071]

[0072] Where θ is the motor rotor position observation value obtained by using two Hall sensors. Since the two Hall sensors are distributed at a 90° electrical angle in space, the Hall signals obtained respectively can be regarded as the sine and cosine signals of the motor rotor position observation value. The motor rotor position observation value can be obtained by combining the two. a0 and b0 are the zero drift values ​​of the two Hall sensors. a n and b n are the 2n-1th harmonic amplitudes of the magnetic field of the two Hall sensors respectively; e d The error caused by the zero drift of the Hall sensor; e c is the error term generated by the excitation magnetic field of the motor winding; e h is the error term generated by the magnetic field harmonics; e r The error term caused by the radial installation error of the Hall sensor and the difference in the amplification factor; e a The error term caused by the axial installation error of the Hall sensor; e f is the non-uniform error term of the permanent magnet magnetization intensity of the motor, where p nj With q nj is the amplitude of the fractional harmonics of the two Hall signal errors, and the amplitude of the fractional harmonics of the Hall signal f nj , the Hall sensor axial installation error θ0 and the Hall sensor amplification coefficient difference λ are jointly determined, i q is the motor q-axis current, and h0 is the magnetic field harmonic amplitude generated by unit current.

[0073] Furthermore, each error term can be simplified as follows:

[0074] For the error term e generated by the magnetic field harmonics h , only consider the influence of n+1=3, 5, 7 harmonics, among which the error expressions of 3rd and 5th harmonics are the 4th harmonic functions of the motor rotor position, and the 7th harmonic produces the 8th harmonic error related to the motor rotor position; for the error term e caused by the radial installation error and the difference in the amplification factor of the Hall sensor r and the error term e caused by the axial installation error of the Hall sensor a , only the error caused by the fundamental wave in the expression is compensated; while the non-uniform error of the permanent magnet magnetization intensity of the motor e f The impact on the measurement accuracy of the motor rotor position is small, so its compensation is not considered.

[0075] Based on the above simplification, the expressions of each element in the weight vector w are as follows:

[0076] w0=-h0

[0077] w1=λb0

[0078] w2=-a0

[0079]

[0080]

[0081]

[0082]

[0083] Wherein, a1 and b1 are the first harmonic amplitudes of the magnetic flux density of the two Hall sensors, and a2 and b2 are the third harmonic amplitudes of the magnetic flux density of the two Hall sensors.

[0084] In some embodiments, to ensure that the Adaline network can effectively compensate for the motor's position error, it is necessary to ensure that the network's weights converge to the actual coefficients of the error terms. Data points are uniformly sampled over one motor's motion cycle as a training set for the Adaline network. Convergence analysis is performed on the Adaline network to ensure that the motor's position error converges to the actual error in the motor's position.

[0085] In some embodiments, the training objective of the motor mover position measurement accuracy compensation neural network is to minimize the sum of squares of the motor mover estimation error output by the neural network and the actual position error value, that is:

[0086]

[0087] Where d(k) and y(k) are the motor rotor position estimation errors output by the motor rotor position measurement accuracy compensation neural network, respectively, and k is the number of iterations. J(w) is the sum of the squares of the errors between the two.

[0088] In some embodiments, the Lumberg observer is used to correct the motor rotor position observation value obtained by the Hall sensor, which is recorded as It can be expressed as:

[0089]

[0090] Where m is the mass of the motor rotor; s is the complex variable of the transfer function; k p 、k i 、k d is the gain of the Lumberg observer. In order to ensure the stability of the Lumberg observer, the poles of the Lumberg observer are set to triple roots. At this time, k i =-mγ 3 , k p =3mγ 2 , k d =-3γ, γ is The bandwidth of the Lumberg observer can be set by adjusting the value of γ.

[0091] The corrected motor rotor position observation value obtained by the Lumberg observer is used instead of the accurate value to be input into the motor rotor position measurement accuracy compensation neural network, and the motor rotor position after error compensation is obtained by subtracting the motor rotor position estimation error.

[0092] When two Hall sensors spaced 90° apart are used to measure the position of a cylindrical permanent magnet synchronous linear motor's rotor, position estimation accuracy is easily degraded by factors such as magnetic field harmonics, sensor zero-point drift and amplification factor differences, sensor installation errors, motor temperature, and winding current. Therefore, compensating for these errors in linear motor rotor position estimation can effectively improve Hall sensor position detection accuracy and meet the requirements of servo systems.

[0093] The effectiveness of the motor rotor position measurement accuracy compensation method provided by the embodiment of the present disclosure is verified below:

[0094] The position observation value of the motor rotor including harmonic error is obtained based on the Lumberg observer The q-axis current sampling value of the permanent magnet synchronous linear motor and the motor rotor position observation value are input into the trained motor rotor position measurement accuracy compensation neural network, and the motor rotor position estimation error is output. The estimated position of the motor and the estimated error value Subtract to get the position after error compensation

[0095] Under four different experimental conditions:

[0096] C1: The maximum speed of the motor is 20 mm / s, and the maximum acceleration is 0.8 m / s 2 , no-load operation, motor operating temperature is 25℃

[0097] C2: The maximum speed of the motor is 20 mm / s, and the maximum acceleration is 0.8 m / s 2 , the load force is 10N, the motor operating temperature is 25℃

[0098] C3: The maximum speed of the motor is 20 mm / s, and the maximum acceleration is 0.8 m / s 2 , the load force is 10N, the motor operating temperature is 55℃

[0099] C4: The maximum speed of the motor is 400 mm / s and the maximum acceleration is 3 m / s 2 , the load force is 10N, the motor operating temperature is 25℃

[0100] The motor rotor position calculation result and position estimation error are as follows Figure 2 As shown, (a1) to (d1) are the curves of the command position of the motor rotor changing with time, and (a2) to (d2) are the curves of the position estimation error of the motor rotor changing with time. The maximum estimation error of the motor rotor position is 113 μm, which is a decrease of 69.7% compared with before compensation. The maximum value of the estimation error is basically consistent with the root mean square under different experimental conditions, and it has high robustness to the influence of factors such as temperature, load, and speed.

[0101] See also Figure 3 The second embodiment of the present disclosure provides a motor mover position measurement accuracy compensation device, comprising:

[0102] A construction module is used to construct a motor mover position measurement accuracy compensation neural network, whose input is the error term related variables of various influencing factors obtained based on the motor mover position estimation value and the motor q-axis current, and the output is the motor mover position estimation error;

[0103] The acquisition module is used to obtain the motor mover position observation value through the Hall sensor, collect the actual position and command position of the motor mover, and together form the training set;

[0104] A training module is used to train the motor mover position measurement accuracy compensation neural network using a training set to obtain a trained motor mover position measurement accuracy compensation neural network;

[0105] The compensation module is used to input the variables related to the error terms obtained according to the position observation value of the motor mover into the trained motor mover position measurement accuracy compensation neural network, output the motor mover position estimation error, and thereby perform error compensation on the motor mover position observation value.

[0106] In order to implement the above embodiment, the embodiment of the present disclosure further proposes a computer-readable storage medium on which a computer program is stored. The program is executed by a processor to perform the Hall sensor position measurement accuracy compensation method of the above embodiment.

[0107] Reference below Figure 4 , which shows a schematic structural diagram of an electronic device 100 suitable for implementing the embodiments of the present disclosure. It should be noted that the electronic device 100 in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs, desktop computers, servers, and the like. Figure 4 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0108] like Figure 4 As shown, the electronic device 100 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 102 or a program loaded from a storage device 108 into a random access memory (RAM) 103. Various programs and data required for the operation of the electronic device 100 are also stored in the RAM 103. The processing device 101, the ROM 102, and the RAM 103 are connected to each other via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0109] Typically, the following devices may be connected to the I / O interface 105: an input device 106 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, etc.; an output device 107 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 108 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 109. The communication device 109 may allow the electronic device 100 to communicate with other devices wirelessly or by wire to exchange data. Figure 4 The electronic device 100 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0110] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, this embodiment includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication device 109, or installed from the storage device 108, or installed from the ROM 102. When the computer program is executed by the processing device 101, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0111] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0112] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0113] The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: constructs a motor rotor position measurement accuracy compensation neural network, the input of the motor rotor position measurement accuracy compensation neural network is the error term related variables of each influencing factor obtained according to the motor rotor position estimation value and the motor q-axis current, and the output is the motor rotor position estimation error; uses the Hall sensor to obtain the motor rotor position observation value, and collects the actual position and command position of the motor rotor to jointly constitute a training set; uses the training set to train the motor rotor position measurement accuracy compensation neural network to obtain a trained motor rotor position measurement accuracy compensation neural network; inputs the various error term related variables obtained according to the motor rotor position observation value and the motor q-axis current into the trained motor rotor position measurement accuracy compensation neural network, outputs the motor rotor position estimation error, and thereby performs error compensation on the motor rotor position observation value.

[0114] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, Python, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0115] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0116] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0117] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0118] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection having one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.

[0119] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0120] Those skilled in the art will understand that all or part of the steps carried out in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the developed program can be stored in a computer-readable storage medium. When the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0121] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0122] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for compensating the position measurement accuracy of a motor rotor, characterized in that: include: Constructing a motor mover position measurement accuracy compensation neural network, wherein the input of the motor mover position measurement accuracy compensation neural network is the error term related variables of various influencing factors obtained according to the motor mover position estimation value and the motor q-axis current, and the output is the motor mover position estimation error; The Hall effect sensor is used to obtain the observed value of the motor rotor position, and the actual position and command position of the motor rotor are collected to form a training set; Using the training set to train the motor mover position measurement accuracy compensation neural network to obtain a trained motor mover position measurement accuracy compensation neural network; The variables related to the error terms obtained based on the motor rotor position observation value and the motor q-axis current are input into the trained motor rotor position measurement accuracy compensation neural network, and the motor rotor position estimation error is output to compensate for the error of the motor rotor position observation value; The motor rotor position measurement accuracy compensation method is applied to the case where the position of a cylindrical permanent magnet synchronous linear motor rotor is measured using two Hall sensors spaced 90 degrees apart in electrical angle. The motor rotor position measurement accuracy compensation neural network constructed adopts an Adaline neural network, and the relationship between its input and output satisfies the following formula: y=w T x x=[i q sinθ cosθ sin2θ cos2θ sin4θ sin8θ] T <h2 style=";text-align:left;direction:ltr">w=[w0 w1 w2 w3 w4 w5 w6]<h2 style=";text-align:left;direction:ltr"> T Among them, x is the input of the motor rotor position measurement accuracy compensation neural network, which is a vector whose elements represent the relevant variables composed of q-axis current and motor rotor estimated position sine and cosine in the error terms caused by the motor winding excitation magnetic field, Hall sensor zero drift, magnetic field harmonics, Hall sensor radial installation error and amplification factor difference, Hall sensor axial installation error, and uneven magnetization intensity of the motor permanent magnet, and θ is the motor rotor position observation value; w is the weight vector composed of the weight coefficients of the motor rotor position measurement accuracy compensation neural network, and each weight coefficient w0~w6 represents the coefficient of the relevant variable of each error term; y is the output of the motor rotor position measurement accuracy compensation neural network, that is, the motor rotor position estimation error The expressions of each element in the weight vector w are as follows: w0=-h0 w1=λb0 w2=-a0 Where h0 is the magnetic flux harmonic amplitude generated by the unit current; a0 and b0 are the zero drift values ​​of the two Hall sensors; a1 and b1 are the first magnetic flux harmonic amplitudes of the two Hall sensors, and a2 and b2 are the third magnetic flux harmonic amplitudes of the two Hall sensors; λ is the difference in the Hall sensor amplification factor; θ0 is the axial installation error of the Hall sensor.

2. The motor rotor position measurement accuracy compensation method according to claim 1, characterized in that: The motor rotor position estimation error Expressed as: in: And d =λb0sinθ-a0cosθ and c =-h0i q Where θ is the observed value of the motor rotor position, obtained using two Hall sensors; a n and b n are the 2n-1th harmonic amplitudes of the magnetic field of the two Hall sensors respectively; e d The error caused by the zero drift of the Hall sensor; e c is the error term generated by the excitation magnetic field of the motor winding; e h is the error term generated by the magnetic field harmonics; e r The error term caused by the radial installation error of the Hall sensor and the difference in the amplification factor; e a The error term caused by the axial installation error of the Hall sensor; e f is the non-uniform error term of the permanent magnet magnetization intensity of the motor, where p nj With q nj is the amplitude of the fractional harmonics of the two Hall signal errors, and the amplitude of the fractional harmonics of the Hall signal f nj , the Hall sensor axial installation error θ0 and the Hall sensor amplification coefficient difference λ are jointly determined, i q is the motor q-axis current.

3. The motor rotor position measurement accuracy compensation method according to claim 2, characterized in that: The error terms are simplified as follows: The error term e generated by the magnetic field harmonics h , only consider the influence of n+1=3,5,7 harmonics, among which the error expressions of 3rd and 5th harmonics are the 4th harmonic functions of the motor rotor position, and the 7th harmonic produces the 8th harmonic error related to the motor rotor position; for the error term e caused by the radial installation error and the difference in the amplification factor of the Hall sensor r and the error term e caused by the axial installation error of the Hall sensor a , only the error caused by the fundamental wave in the expression is compensated; the uneven error of the magnetization intensity of the permanent magnet of the motor e is ignored f Impact on the measurement accuracy of the motor rotor position; Based on the above simplification, the expression of each element in the weight vector w is obtained.

4. The motor rotor position measurement accuracy compensation method according to claim 1, characterized in that: Data points are uniformly sampled during one motion cycle of the motor as the training set of the Adaline network.

5. The motor rotor position measurement accuracy compensation method according to claim 1, characterized in that: The training objective of the motor mover position measurement accuracy compensation neural network is to minimize the sum of squares of the motor mover estimation error output by the neural network and the actual position error value.

6. The motor rotor position measurement accuracy compensation method according to claim 1, characterized in that: The motor mover position measurement accuracy compensation method further includes: using a Romberg observer to correct the motor mover position observation value obtained by the Hall sensor.

7. A motor mover position measurement accuracy compensation device based on the motor mover position measurement accuracy compensation method according to claim 1, characterized in that: include: A construction module is used to construct a motor mover position measurement accuracy compensation neural network, wherein the input of the motor mover position measurement accuracy compensation neural network is the error term related variables of various influencing factors obtained according to the motor mover position estimation value and the motor q-axis current, and the output is the motor mover position estimation error; The acquisition module is used to obtain the motor mover position observation value through the Hall sensor, collect the actual position and command position of the motor mover, and together form the training set; A training module, configured to train the motor mover position measurement accuracy compensation neural network using the training set to obtain a trained motor mover position measurement accuracy compensation neural network; The compensation module is used to input the variables related to the error terms obtained based on the position observation value of the motor rotor and the motor q-axis current into the trained motor rotor position measurement accuracy compensation neural network, and output the motor rotor position estimation error, thereby compensating the error of the motor rotor position observation value.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the motor mover position measurement accuracy compensation method according to any one of claims 1 to 6.

Images