A two-phase hybrid stepping motor position sensorless load angle estimation and stall detection method

Abstract

The application provides a kind of two-phase hybrid stepping motor position sensorless load angle estimation and stall detection method, it is related to motor control technical field.First, the phase voltage and phase current of motor are collected, the continuous smooth back electromotive force signal is obtained by constructing supercoil sliding mode observer, and the adaptive strategy of introducing resistance parameter online compensation and sliding mode coefficient online adjustment is introduced to improve the robustness of sliding mode observer.Then, the back electromotive force phase angle is extracted by using the quadrature phase-locked loop based on extended state observer, and the load angle is calculated by combining the phase of stator reference current.Finally, the load angle is compared with the multi-stage early warning threshold, the real-time monitoring of motor load and the rapid detection of out-of-step stall fault are realized.The application eliminates the hardware dependence of traditional load angle calculation and stall detection on position sensor, effectively overcomes the problems of high-frequency differential noise, filter phase lag and other problems in sensorless algorithm, and realizes high-precision load angle estimation and stall detection.

Description

Technical Field

[0001] This invention relates to the field of motor control technology, and in particular to a sensorless load angle estimation and stall detection method for a two-phase hybrid stepper motor. Background Technology

[0002] Stepper motors occupy an important position in aerospace, military, and intelligent manufacturing fields due to their advantages such as simple structure, high-precision open-loop positioning, and torque retention even when power is off. For example, solar array drive mechanisms (SADA) driven by stepper motors have been widely used in aerospace fields such as satellites.

[0003] Stepper motors possess excellent open-loop control characteristics: the controller only needs to send step pulses to drive the rotor to rotate at a predetermined angle. This characteristic makes it the preferred solution in cost- and size-sensitive applications. However, traditional open-loop control has inherent limitations, namely, the controller cannot obtain the actual position of the rotor. In stall conditions, if the controller fails to recognize the stall and continues to send pulses, it will cause motor vibration and overheating, severely reducing the reliability of the motor system. Therefore, to reduce the risk of step loss, derating designs are usually adopted in engineering, such as selecting a motor with higher torque, limiting the maximum speed or load torque, etc., resulting in the motor system operating at suboptimal efficiency.

[0004] The load angle is a crucial physical quantity characterizing the operating state and torque margin of a stepper motor. As the load torque increases, the load angle also increases; once the load angle exceeds 90°, the motor may lose steps or even stall. If the load angle can be acquired in real time, the controller can accurately assess the motor's current load level, thus providing early warning and protection before a fault occurs. Therefore, real-time and accurate acquisition of the load angle for load status monitoring and stall detection is one of the key technologies in the field of stepper motor control.

[0005] Existing load angle estimation and stall detection technologies can be mainly divided into the following two categories:

[0006] 1. Detection method based on mechanical position sensor:

[0007] The rotor position is directly measured by installing position sensors, and the load angle is then calculated. While accurate, this method has several drawbacks: First, high-precision position sensors are often more expensive than the motor itself, increasing the axial dimensions and wiring complexity of the motor system. Second, the sensor's precision structure makes its vibration resistance and resistance to high and low temperatures far inferior to the motor itself. In extreme environments (such as aerospace environments), the sensor is highly susceptible to failure, which actually reduces the system's reliability.

[0008] 2. Detection method based on back electromotive force:

[0009] This method calculates the back electromotive force (EMF) using the current equation and then extracts the rotor position information through a phase-locked loop (PLL) to solve for the load angle. However, this method has the following limitations: First, the voltage equation contains a current differential term, and direct differentiation amplifies high-frequency noise, leading to severe jitter in the load angle estimation results. Second, it is sensitive to motor parameters; in actual operation, the stator resistance changes with temperature, and parameter mismatch can cause deviations in the back EMF and load angle estimations, potentially leading to misjudgments of the motor load state. Furthermore, existing phase extraction methods often use PLLs based on proportional-integral (PI) controllers, which struggle to achieve zero steady-state error tracking under dynamic conditions such as rapid acceleration and deceleration, causing the phase to lag behind the actual rotor position and resulting in inconsistencies between the estimated load angle and the actual state.

[0010] In summary, how to effectively suppress motor parameter mismatch without the need for position sensors, and accurately estimate the load angle during dynamic acceleration and deceleration, has become a key technical problem that urgently needs to be solved. Summary of the Invention

[0011] To address the shortcomings of existing technologies, this invention provides a sensorless load angle estimation and stall detection method for a two-phase hybrid stepper motor. It aims to solve problems such as load angle estimation distortion and stall misjudgment caused by current differential noise, filter phase lag, and motor parameter temperature drift. First, a super-spiral sliding mode observer (STASMO) is constructed to estimate the motor back EMF, effectively avoiding noise from direct differentiation. Second, a sliding mode coefficient dynamically adjusted with speed is introduced to balance low-speed chatter reduction and high-speed stability. Simultaneously, a resistance adaptive compensation algorithm is used to correct model parameters, improving the observer's robustness to temperature changes. Finally, an orthogonal phase-locked loop (QPLL) based on an extended state observer (ESO) is used to extract the back EMF phase, and the load angle is calculated by combining vector relationships. The load angle is then used to achieve accurate load state monitoring and stall judgment.

[0012] The technical solution of the present invention is as follows:

[0013] On one hand, the present invention provides a method for sensorless load angle estimation and stall detection of a two-phase hybrid stepper motor, comprising the following steps:

[0014] Step 1: Establish a back EMF observer model and obtain the stator back EMF estimate: Real-time acquisition of the motor's phase current and phase voltage; and based on the current equation in the stator's two-phase stationary coordinate system, establish a super-helical sliding mode observer model, and then calculate the estimated back EMF. and ;

[0015] Step 1 specifically includes the following steps:

[0016] Step 1.1: In the stationary coordinate system ab, the current equation of the two-phase hybrid stepper motor is expressed as:

[0017] (1);

[0018] Among them, i a i b These are the stator currents for phase a and phase b, respectively; u a u b These are the stator voltages for phase a and phase b, respectively; e a e b These are the stator back EMFs of phase a and phase b, respectively; R is the stator resistance, and L is the stator inductance.

[0019] Step 1.2: Set the stator back electromotive force e a and e b The relationship with the rotor position is expressed as follows:

[0020] (2);

[0021] Among them, K m θ is the back potential coefficient. e The phase angle of the back electromotive force;

[0022] Step 1.3: Using the estimated value of the stator current and As state variables, the back potential estimation equation based on the superspiral sliding mode observer is established as follows:

[0023] (3);

[0024] Where k1 and k2 are sliding mode coefficients, and sgn(⋅) is the sign function;

[0025] Step 1.4: Estimated value of back electromotive force after the sliding mode observer reaches the sliding surface. and Represented as:

[0026] (4);

[0027] Step 2: Introducing an adaptive parameter correction mechanism: During the operation of the super-helical sliding mode observer, an adaptive strategy is implemented to compensate for the resistance parameters online and adjust the sliding mode coefficient online. First, based on the current estimation error of the observer, an adaptive law is established to compensate for the stator resistance parameters online, and the updated resistance parameters are fed back to the observer model in real time. At the same time, the sliding mode coefficient of the super-helical sliding mode observer is dynamically adjusted according to the commanded speed of the motor.

[0028] The online compensation of the resistance parameter specifically involves:

[0029] Introducing the parameter adaptive law:

[0030] (5);

[0031] Where γ is the gain of the resistance adaptive law, The stator resistance is estimated at time k; the resistance parameters in the observer are corrected in real time using the parameter adaptive law.

[0032] The online adjustment of the sliding mode coefficient specifically refers to:

[0033] The sliding mode coefficients k1 and k2 are adjusted online according to the rotational speed, and the adjustment relationship is as follows:

[0034] (6);

[0035] Where β1 and β2 are the gains of the adaptive law of sliding mode coefficients, ω cmd The commanded speed of the motor is ω. cmd It is derived by converting the given frequency of the pulse;

[0036] Step 3: Extract the phase of the back potential: Construct an orthogonal phase-locked loop based on an extended state observer; extract the back potential estimate. and As input, the phase error information is calculated by the phase detector of the quadrature phase-locked loop, and the back EMF phase angle is estimated and output using the extended state observer. ;

[0037] Step 3.1: Estimate the back electromotive force. and The input is given to the phase detector, and the back EMF signal is used to construct the following phase angle estimation error. :

[0038] (7);

[0039] in, This is an estimate of the back EMF phase angle;

[0040] Step 3.2: Establish a phase angle convergence system to bring the estimation error of the back EMF phase angle to zero; define the system input variable as the estimated rotational speed u. e The state equation of the phase angle convergent system is then expressed as:

[0041] (8);

[0042] Where b represents the system gain and d is the lumped disturbance of the system;

[0043] Define d εTo account for the lumped disturbances during the tracking error process, the dynamic equation of the tracking error is expressed as:

[0044] (9);

[0045] Step 3.3: Combine the lumped disturbance d ε Expanding to new state variables, a second-order extended state observer is established:

[0046] (10);

[0047] in, yes The estimated value, It is d ε The estimated value, the observer gain is configured as follows: , ω0 is the bandwidth of ESO;

[0048] Step 3.4: Construct a feedback control law and perform integral calculations to obtain the estimated value of the back EMF phase angle. :

[0049] (11);

[0050] Where k0 is the proportional gain of the feedback control law.

[0051] Step 4: Calculate the load angle: Obtain the phase angle θ of the phase current based on the stator reference current. ph Combined with the back electromotive force phase angle estimate The real-time load angle δ is calculated using the vector relationship of the stepper motor;

[0052] Specifically, based on the phase angle θ of the stator reference current. ph and estimated value of back electromotive force phase angle Based on the characteristic that the back EMF vector leads the rotor flux linkage by 90° electrical angle in space, the real-time load angle δ is calculated using the following formula:

[0053] (12);

[0054] Step 5: Load Status Monitoring and Stall Judgment: The current load status is characterized by the real-time load angle δ obtained in Step 4; a first load angle threshold δ is set. warn and greater than the first load angle threshold δ warn The second load angle threshold δ stop Real-time comparison of the load angle δ with the two thresholds: when δ exceeds the first load angle threshold δ... warn When δ exceeds the second load angle threshold δ, an early warning signal is output; stopWhen this occurs, the system determines that the motor is stalled and outputs a shutdown protection signal.

[0055] Specifically, after calculating the real-time load angle δ of the stepper motor, the following status monitoring is performed using the physical meaning of the real-time load angle δ:

[0056] Step 5.1: Load rate quantification: Based on the real-time load angle δ, the load rate of the current stepper motor system is output in real time using formula (13):

[0057] (13);

[0058] Step 5.2: Set multi-level thresholds: Set the first load angle threshold δ warn Second load angle threshold δ stop The second load angle threshold is greater than the first load angle threshold;

[0059] Step 5.3: Overload warning monitoring: When the real-time load angle δ is greater than the first load angle threshold δ warn When overload warning signal is output;

[0060] Step 5.4: Stall protection: When any or a combination of the following conditions are met, the motor is determined to be stalled, the drive output is cut off, and a shutdown protection signal is generated.

[0061] (1) The real-time load angle δ is greater than the second load angle threshold δ stop ;

[0062] (2) The pulse frequency command output by the motor driver is non-zero, but the observed back EMF amplitude is consistently lower than the set minimum back EMF threshold E. min And it exceeded the set time.

[0063] On the other hand, this application proposes a computer-readable storage medium storing executable instructions that, when executed, cause a processor to perform the described method for sensorless load angle estimation and stall detection of a two-phase hybrid stepper motor.

[0064] Thirdly, this application proposes a computer program product, including a computer program or instructions, which, when executed by a processor, implements the aforementioned method for sensorless load angle estimation and stall detection of a two-phase hybrid stepper motor.

[0065] The beneficial effects of adopting the above technical solution are as follows:

[0066] This invention provides a sensorless load angle estimation and stall detection method for a two-phase hybrid stepper motor, which has the following advantages:

[0067] (1) This invention does not require mechanical position sensors. It can achieve multiple monitoring functions such as load rate quantification, overload warning and stall protection by estimating the load angle through algorithms, thereby reducing hardware costs and improving the reliability of the stepper motor system.

[0068] (2) The present invention uses STASMO to estimate the back EMF, which avoids the risk of noise amplification caused by direct differentiation, eliminates the phase lag problem introduced by the traditional sliding mode observer which requires a low-pass filter to filter out chattering, and significantly improves the observation accuracy of back EMF under dynamic conditions.

[0069] (3) This invention introduces an online resistance compensation and sliding mode coefficient adaptive adjustment strategy. By using the current observation error to correct the resistance parameters in the model in real time, the resistance drift caused by the motor temperature rise is effectively compensated; at the same time, the sliding mode coefficient is dynamically adjusted according to the speed, which solves the contradiction that fixed gain cannot take into account both low-speed chatter suppression and high-speed stable convergence, and ensures the operating stability of the motor system in a wide speed range.

[0070] (4) This invention constructs an orthogonal phase-locked loop based on ESO. By utilizing the observation and compensation capabilities of ESO for lumped disturbances, the phase tracking lag of traditional PI phase-locked loops during motor acceleration and deceleration is effectively overcome, and fast, zero steady-state error tracking of the back EMF phase is achieved. Attached Figure Description

[0071] Figure 1 This is a flowchart illustrating the implementation of the method of the present invention;

[0072] Figure 2 This is a structural block diagram of the superspiral sliding mode back EMF observer in an embodiment of the present invention;

[0073] Figure 3 This is a block diagram illustrating the principle of constructing phase angle estimation error based on back EMF signal in an embodiment of the present invention.

[0074] Figure 4 This is a block diagram of the orthogonal phase-locked loop principle based on the extended state observer in an embodiment of the present invention;

[0075] Figure 5 This is a general block diagram of the control system in an embodiment of the present invention;

[0076] Figure 6 This is a comparison diagram of the load angle estimation waveform and the actual value of the method of the present invention under a test environment;

[0077] Figure (a) shows the estimated speed waveform for a motor speed of 100 r / min and a load torque of 0.4 Nm; Figure (b) shows the estimated speed waveform for a motor speed of 200 r / min and a load torque of 0.6 Nm; Figure (c) shows the back EMF estimation waveform for a motor speed of 100 r / min and a load torque of 0.4 Nm; Figure (d) shows the back EMF estimation waveform for a motor speed of 200 r / min and a load torque of 0.6 Nm; Figure (e) shows the load angle estimation waveform for a motor speed of 100 r / min and a load torque of 0.4 Nm; and Figure (f) shows the load angle estimation waveform for a motor speed of 200 r / min and a load torque of 0.6 Nm.

[0078] Figure 7 The waveform diagram shows the stall detection and protection of the method of the present invention under test environment. Detailed Implementation

[0079] The specific implementation methods of this application will be further described in detail below with reference to the accompanying drawings and embodiments.

[0080] Example 1:

[0081] On the one hand, this invention provides a sensorless load angle estimation and stall detection method for a two-phase hybrid stepper motor, such as... Figure 1 As shown, it includes the following steps:

[0082] Step 1: Establish a back EMF observer model and obtain the stator back EMF estimate: Real-time acquisition of the motor's phase current and phase voltage; and based on the current equation in the stator's two-phase stationary coordinate system, establish a super-helical sliding mode observer model, and then calculate the estimated back EMF. and ;

[0083] Step 1 specifically includes the following steps:

[0084] Step 1.1: In the stationary coordinate system ab, the current equation of the two-phase hybrid stepper motor is expressed as:

[0085] (1);

[0086] Among them, i a i b These are the stator currents for phase a and phase b, respectively; u a u b These are the stator voltages for phase a and phase b, respectively; e a e b These are the stator back EMFs of phase a and phase b, respectively; R is the stator resistance, and L is the stator inductance.

[0087] Step 1.2: Set the stator back electromotive force e a and eb The relationship with the rotor position is expressed as follows:

[0088] (2);

[0089] Among them, K m θ is the back potential coefficient. e The phase angle of the back electromotive force;

[0090] Step 1.3: Using the estimated value of the stator current and As state variables, the back potential estimation equation based on the superspiral sliding mode observer is established as follows:

[0091] (3);

[0092] Where k1 and k2 are sliding mode coefficients, and sgn(⋅) is the sign function;

[0093] Step 1.4: The implementation block diagram of the STASMO-based back potential estimation designed in this embodiment is as follows: Figure 2 As shown. Compared to traditional sliding mode observers, the super-spiral sliding mode observer integrates the discontinuous sign function, obtaining a smooth back EMF estimate without a low-pass filter, effectively avoiding the phase lag problem caused by filters;

[0094] Once the sliding mode observer reaches the sliding surface, the estimated value of the back potential is... and Represented as:

[0095] (4);

[0096] Step 2: Introducing an adaptive parameter correction mechanism: During the operation of the super-helical sliding mode observer, an adaptive strategy is implemented to compensate for the resistance parameters online and adjust the sliding mode coefficient online. First, based on the current estimation error of the observer, an adaptive law is established to compensate for the stator resistance parameters online, and the updated resistance parameters are fed back to the observer model in real time. At the same time, the sliding mode coefficient of the super-helical sliding mode observer is dynamically adjusted according to the commanded speed of the motor.

[0097] The online compensation of the resistance parameter specifically involves:

[0098] Introducing the parameter adaptive law:

[0099] (5);

[0100] Where γ is the gain of the resistance adaptive law, The stator resistance is estimated at time k; the resistance parameters in the observer are corrected in real time using the parameter adaptive law to eliminate the influence of parameter mismatch on the back potential estimation.

[0101] The online adjustment of the sliding mode coefficient specifically refers to:

[0102] The sliding mode coefficients k1 and k2 are adjusted online according to the rotational speed to meet the requirements of low-speed chatter suppression and high-speed stability. The adjustment relationship is as follows:

[0103] (6);

[0104] Where β1 and β2 are the gains of the adaptive law of sliding mode coefficients, ω cmd The commanded speed of the motor is ω. cmd It is derived by converting the given frequency of the pulse;

[0105] Step 3: Extract the phase of the back EMF: Construct an orthogonal phase-locked loop (ESO-QPLL) based on an extended state observer; extract the back EMF estimate. and As input, the phase error information is calculated by the phase detector of the quadrature phase-locked loop (QPLL), and the back electromotive force phase angle estimate is estimated and output using the extended state observer (ESO). ;

[0106] Step 3.1: First, construct the phase angle estimation error based on the back EMF signal. The principle block diagram is as follows: Figure 3 As shown; the estimated value of the back electromotive force. and The input is fed to the phase detector, where it is multiplied by the feedback sine and cosine signals respectively. The phase angle estimation error is then constructed using the back EMF signal. Through formula Phase angle estimation error Through derivation, we obtain the following formula:

[0107] (7);

[0108] in, This is an estimate of the back EMF phase angle;

[0109] Step 3.2: Since a closed-loop controller is used to control the convergence of the position system, that is, the estimation error of the back EMF phase angle is brought to zero through the controller, so that the estimated phase angle value can track the true value of the phase angle.

[0110] Establish a phase angle convergence system such that the estimation error of the back EMF phase angle converges to zero; define the system input variable as the estimated rotational speed u. e The state equation of the phase angle convergent system is then expressed as:

[0111] (8);

[0112] Where b represents the system gain and d is the lumped disturbance of the system;

[0113] Define d ε To account for the lumped disturbances during the tracking error process, the dynamic equation of the tracking error is expressed as:

[0114] (9);

[0115] Step 3.3: Traditional quadrature phase-locked loops (PLLs) typically use a PI controller to extract the phase angle from the estimation error. However, various uncertainties and unknown disturbances exist in stepper motor systems, making it difficult for the PI controller to provide accurate estimates. Therefore, it is proposed to use an ESO (Electronic Stability Loop) to replace the PI controller to address these uncertainties and unknown disturbances.

[0116] The ESO-QPLL principle block diagram is as follows: Figure 4 As shown, the lumped disturbance d ε Expanding to new state variables, a second-order extended state observer is established:

[0117] (10);

[0118] in, yes The estimated value, It is d ε The estimated value, the observer gain is configured as follows: , ω0 is the bandwidth of ESO;

[0119] Step 3.4: Construct a feedback control law and perform integral calculations to obtain the estimated value of the back EMF phase angle. :

[0120] (11);

[0121] Where k0 is the proportional gain of the feedback control law.

[0122] Step 4: Calculate the load angle: Obtain the phase angle θ of the phase current based on the stator reference current. ph Combined with the back electromotive force phase angle estimate The real-time load angle δ is calculated using the vector relationship of the stepper motor;

[0123] Specifically, based on the phase angle θ of the stator reference current. ph and estimated value of back electromotive force phase angle Based on the characteristic that the back EMF vector leads the rotor flux linkage by 90° electrical angle in space, the real-time load angle δ is calculated using the following formula:

[0124] (12);

[0125] Step 5: Load Status Monitoring and Stall Judgment: The current load status is characterized by the real-time load angle δ obtained in Step 4; a first load angle threshold δ is set. warn and greater than the first load angle threshold δ warn The second load angle threshold δ stop Real-time comparison of the load angle δ with the two thresholds: when δ exceeds the first load angle threshold δ... warn When δ exceeds the second load angle threshold δ, an early warning signal is output; stop When this occurs, the system determines that the motor is stalled and outputs a shutdown protection signal.

[0126] Specifically, after calculating the real-time load angle δ of the stepper motor, the following status monitoring is performed using the physical meaning of the real-time load angle δ:

[0127] Step 5.1: Load rate quantification: Based on the real-time load angle δ, the load rate of the current stepper motor system is output in real time using formula (13):

[0128] (13);

[0129] Step 5.2: Set multi-level thresholds: Set the first load angle threshold δ warn Second load angle threshold δ stop The second load angle threshold is greater than the first load angle threshold;

[0130] In this embodiment, the first load angle threshold δ warn The settable range is 70°~75°; the second load angle threshold δ stop The settable range is 80°~85°; it can be adjusted according to the specific characteristics of different motor systems; in this embodiment, a first load angle threshold δ is set. warn =72°, set the second load angle threshold δ stop =82°;

[0131] Step 5.3: Overload warning monitoring: When the real-time load angle δ is greater than the first load angle threshold δ warn When overload warning signal is output;

[0132] Step 5.4: Stall protection: When any or a combination of the following conditions are met, the motor is determined to be stalled, the drive output is cut off, and a shutdown protection signal is generated.

[0133] (1) The real-time load angle δ is greater than the second load angle threshold δ stop ;

[0134] (2) The pulse frequency command output by the motor driver is non-zero, but the observed back EMF amplitude is consistently lower than the set minimum back EMF threshold E. min And it exceeded the set time.

[0135] Once the stall protection condition is met, the drive output is quickly cut off, and a shutdown protection signal is generated to prevent motor vibration or overheating, thereby protecting the motor body and drive system.

[0136] Figure 5 This is a general block diagram of the control system in an embodiment of the present invention. The control system is divided into two parts: open-loop drive and closed-loop monitoring. The open-loop drive part generates micro-step current commands based on the pulse input and establishes the stator current vector phase. The stepper motor is driven by a current controller and a dual H-bridge. The closed-loop monitoring section acquires the motor's phase voltage and current in real time, obtains a continuous, chatter-free estimate of the back electromotive force (EMF) through a super-spiral sliding mode observer, and then extracts the estimated phase of the back EMF without steady-state error using an ESO-PLL. Finally, the back electromotive force phase... Phase with stator current Vector calculations are performed to calculate the load angle δ in real time, which is used for load status monitoring and stall detection.

[0137] Example 2:

[0138] To verify the effectiveness of the method proposed in this invention, the algorithm in this embodiment was simulated in the Matlab / Simulink environment. The parameters of the stepper motor under test are shown in Table 1.

[0139] Table 1 Parameters of the tested stepper motor

[0140] variable parameter value <![CDATA[U nom ]]> Rated voltage 24V <![CDATA[T max ]]> Holding torque 0.9Nm <![CDATA[I nom ]]> Rated current 3A <![CDATA[R a ]]> winding resistance 0.7Ω <![CDATA[L a ]]> Winding inductance 1.4mH <![CDATA[J m ]]> Rotor inertia <![CDATA[120g·cm 2 ]]>

[0141] The experimental results and analysis are discussed below:

[0142] (1) Load angle estimation test

[0143] Figure 6 The experimental waveforms of the proposed method under different speeds and load conditions are shown for the stepper motor driven in 1 / 64 microstep mode. As shown in (a)-(f), using the STASMO algorithm, smooth and highly sinusoidal two-way electromotive force waveforms were observed under both conditions. and Furthermore, the two maintain a 90° phase difference. Compared to traditional sliding mode observers, there is almost no high-frequency chattering in the waveform, demonstrating the superiority of the STASMO algorithm in suppressing sliding mode chattering.

[0144] Figure 6The real-time load angle estimate obtained based on the back EMF estimate and current phase is displayed. The load angle estimate remains stable during steady-state motor operation. The results demonstrate that the proposed method can achieve accurate and rapid estimation of the load angle, providing a data foundation for subsequent load monitoring and stall protection.

[0145] (2) Load monitoring and stall protection test

[0146] As the load torque increases, the load angle will increase accordingly, until it reaches the maximum load angle of 90°. At this point, the motor will be unable to drive the load, resulting in loss of synchronization.

[0147] Figure 7 The waveforms shown are from an experiment demonstrating the monitoring and stall protection of the stepper motor during dynamic load changes. The experiment set the target speed of the stepper motor to 200 r / min and the initial load torque to 0.5 Nm. Starting at t=1.5 s, the load torque was continuously increased until the motor lost its steps.

[0148] like Figure 7 As shown, during the period from 0 to 1.5 seconds, the motor is in a steady-state mode with constant speed and load, and the load angle is stable at around 43°. After 1.5 seconds, as the load torque increases linearly, it can be seen that the electromagnetic torque output by the motor continues to rise, and the estimated load angle also shows a significant upward trend, verifying that the load angle can accurately reflect the current load change of the motor.

[0149] This experiment sets a first load angle threshold (warning value) δ warn =72° and the second load angle threshold (stall determination value) δ stop =82°. When t=4.86s, the real-time load angle increases to 72°, triggering the first-level threshold, and the warning signal (red line) is set to high level, indicating system overload. As the load continues to increase, when t=5.178s, the real-time load angle reaches 82°, triggering the second-level threshold, and the stall protection signal (blue line) is set to high level. After t=5.284s, the load angle exceeds the stepper motor's step loss limit (91° in the figure), and the motor loses its steps. This shows that the present invention can predict the risk of stall in advance, providing a buffer time for the driver to take measures to stop or reduce speed before the motor actually loses its steps, thereby achieving effective protection of the motor system.

[0150] Example 3:

[0151] This embodiment proposes a computer-readable storage medium that stores executable instructions. When these instructions are executed, if they are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0152] The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the sensorless load angle estimation and stall detection method for a two-phase hybrid stepper motor described in the various embodiments of this application.

[0153] The aforementioned storage media include: flash memory, hard disk, multimedia card, card-type memory (e.g., SD (Secure Digital Memory Card) or DX (Memory Data Register, MDR) memory, random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, APP (Application) application store, and other media capable of storing program verification codes. These media store computer programs, and when executed by a processor, they can implement the various steps of the aforementioned method for estimating the load angle and detecting stall in a two-phase hybrid stepper motor without a position sensor.

[0154] Example 4:

[0155] This embodiment proposes a computer program product, including a computer program or instructions, which, when executed by a processor, implements the aforementioned method for sensorless load angle estimation and stall detection of a two-phase hybrid stepper motor.

[0156] Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a computer program product.

[0157] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0158] The scope of protection of this application is not limited to the embodiments described above. Obviously, those skilled in the art can make various modifications and variations to this disclosure without departing from the scope and spirit of this disclosure. If such modifications and variations fall within the scope of the methods disclosed herein and their equivalents, then the intent of this disclosure also includes such modifications and variations.

Claims

1. A method for sensorless load angle estimation and stall detection of a two-phase hybrid stepper motor, characterized in that, Includes the following steps: Step 1: Establish a back EMF observer model and obtain the stator back EMF estimate: Real-time acquisition of the motor's phase current and phase voltage; and based on the current equation in the stator's two-phase stationary coordinate system, establish a super-helical sliding mode observer model, and then calculate the estimated back EMF. and ; Step 2: Introducing an adaptive parameter correction mechanism: During the operation of the super-helical sliding mode observer, an adaptive strategy is implemented to compensate for the resistance parameters online and adjust the sliding mode coefficient online. First, based on the current estimation error of the observer, an adaptive law is established to compensate for the stator resistance parameters online, and the updated resistance parameters are fed back to the observer model in real time. At the same time, the sliding mode coefficient of the super-helical sliding mode observer is dynamically adjusted according to the commanded speed of the motor. Step 3: Extract the phase of the back potential: Construct an orthogonal phase-locked loop based on an extended state observer; extract the back potential estimate. and As input, the phase error information is calculated by the phase detector of the quadrature phase-locked loop, and the back EMF phase angle is estimated and output using the extended state observer. ; Step 3 specifically includes the following steps: Step 3.1: Estimate the back electromotive force. and The input is given to the phase detector, and the back EMF signal is used to construct the following phase angle estimation error. : （7）； in, K is the estimated value of the back electromotive force phase angle. m θ is the back potential coefficient. e The phase angle of the back electromotive force; Step 3.2: Establish a phase angle convergence system to bring the estimation error of the back EMF phase angle to zero; define the system input variable as the estimated rotational speed u. e The state equation of the phase angle convergent system is then expressed as: （8）； Where b represents the system gain and d is the lumped disturbance of the system; Define d ε To account for the lumped disturbances during the tracking error process, the dynamic equation of the tracking error is expressed as: （9）； Step 3.3: Combine the lumped disturbance d ε Expanding to new state variables, a second-order extended state observer is established: （10）； in, yes The estimated value, It is d ε The estimated value, the observer gain is configured as follows: , ω0 is the bandwidth of ESO; Step 3.4: Construct a feedback control law and perform integral calculations to obtain the estimated value of the back EMF phase angle. : （11）； Where k0 is the proportional gain of the feedback control law; Step 4: Calculate the load angle: Obtain the phase angle θ of the phase current based on the stator reference current. ph Combined with the back electromotive force phase angle estimate The real-time load angle δ is calculated using the vector relationship of the stepper motor; Step 5: Load Status Monitoring and Stall Judgment: The current load status is characterized by the real-time load angle δ obtained in Step 4; a first load angle threshold δ is set. warn and greater than the first load angle threshold δ warn The second load angle threshold δ stop Real-time comparison of the load angle δ with the two thresholds: when δ exceeds the first load angle threshold δ... warn When δ exceeds the second load angle threshold δ, an early warning signal is output; stop When this occurs, the system determines that the motor is stalled and outputs a shutdown protection signal.

2. The method for sensorless load angle estimation and stall detection of a two-phase hybrid stepper motor according to claim 1, characterized in that, Step 1 specifically includes the following steps: Step 1.1: In the stationary coordinate system ab, the current equation of the two-phase hybrid stepper motor is expressed as: （1）； Among them, i a i b These are the stator currents for phase a and phase b, respectively; u a u b These are the stator voltages for phase a and phase b, respectively; e a e b These are the stator back EMFs of phase a and phase b, respectively; R is the stator resistance, and L is the stator inductance. Step 1.2: Set the stator back electromotive force e a and e b The relationship with the rotor position is expressed as follows: （2）； Step 1.3: Using the estimated value of the stator current and As state variables, the back potential estimation equation based on the superspiral sliding mode observer is established as follows: （3）； Where k1 and k2 are sliding mode coefficients, and sgn(⋅) is the sign function; Step 1.4: Estimated value of back electromotive force after the sliding mode observer reaches the sliding surface. and Represented as: （4）。 3. The method for sensorless load angle estimation and stall detection of a two-phase hybrid stepper motor according to claim 1, characterized in that, The online compensation of resistance parameters in step 2 specifically refers to: Introducing the parameter adaptive law: （5）； Where γ is the gain of the resistance adaptive law, The stator resistance is estimated at time k; the resistance parameters in the observer are corrected in real time using the parameter adaptive law.

4. The method for sensorless load angle estimation and stall detection of a two-phase hybrid stepper motor according to claim 1, characterized in that, The online adjustment of the sliding mode coefficient in step 2 specifically refers to: The sliding mode coefficients k1 and k2 are adjusted online according to the rotational speed, and the adjustment relationship is as follows: （6）； Where β1 and β2 are the gains of the adaptive law of sliding mode coefficients, ω cmd The commanded speed of the motor is ω. cmd It is calculated by converting the given frequency of the pulse.

5. The method for sensorless load angle estimation and stall detection of a two-phase hybrid stepper motor according to claim 1, characterized in that, Step 4 is specifically based on the phase angle θ of the stator reference current. ph and estimated value of back electromotive force phase angle Based on the characteristic that the back EMF vector leads the rotor flux linkage by 90° electrical angle in space, the real-time load angle δ is calculated using the following formula: （12）。 6. The method for sensorless load angle estimation and stall detection of a two-phase hybrid stepper motor according to claim 1, characterized in that, Step 5 specifically involves: after calculating the real-time load angle δ of the stepper motor, performing the following status monitoring using the physical meaning of the real-time load angle δ: Step 5.1: Load rate quantification: Based on the real-time load angle δ, the load rate of the current stepper motor system is output in real time using formula (13): （13）； Step 5.2: Set multi-level thresholds: Set the first load angle threshold δ warn Second load angle threshold δ stop The second load angle threshold is greater than the first load angle threshold; Step 5.3: Overload warning monitoring: When the real-time load angle δ is greater than the first load angle threshold δ warn When overload warning signal is output; Step 5.4: Stall protection: When any or a combination of the following conditions are met, the motor is determined to be stalled, the drive output is cut off, and a shutdown protection signal is generated; (1) The real-time load angle δ is greater than the second load angle threshold δ stop ; (2) The pulse frequency command output by the motor driver is non-zero, but the observed back EMF amplitude is consistently lower than the set minimum back EMF threshold E. min And it exceeded the set time.

7. A computer-readable storage medium, characterized in that, It stores executable instructions that, when executed, cause the processor to perform a method for sensorless load angle estimation and stall detection of a two-phase hybrid stepper motor according to any one of claims 1-6.

8. A computer program product, characterized in that, Includes a computer program or instructions that, when executed by a processor, implement the sensorless load angle estimation and stall detection method for a two-phase hybrid stepper motor according to any one of claims 1-6.

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