A sliding mode observation method and system for sensorless control of permanent magnet synchronous machines

Abstract

The application discloses a sliding mode observation method and system for permanent magnet synchronous motor non-inductive control, which is used for improving the numerical stability and observation accuracy of a discrete system, and realizing the non-inductive closed-loop control of the permanent magnet synchronous motor with good robustness and stability. The method comprises the following steps: discretizing a current state equation according to a sampling period to obtain an initial discrete sliding mode observation equation; determining a proportional adjustment coefficient k based on the inductance parameters of the motor and the sampling period, weighting and adjusting the voltage related term and the sliding mode control term in the initial discrete sliding mode observation equation by using the proportional adjustment coefficient k, and constructing an improved sliding mode observation equation; extracting an extended back electromotive force based on the output of a sliding mode control law, wherein the contribution proportion of the high-frequency chattering component of the sliding mode control law output in the extended back electromotive force is weakened by the proportional adjustment coefficient; and inputting the extended back electromotive force into a phase-locked loop module to calculate the rotor position and speed of the motor to realize the closed-loop control of the motor.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of motor control, and in particular to a sliding mode observation method and system for sensorless control of a permanent magnet synchronous motor. BACKGROUND

[0002] Permanent magnet synchronous motors are widely used in industrial automation, electric vehicles, household appliances and other fields due to their high efficiency, high power density and excellent control performance. Existing permanent magnet synchronous motors usually detect the rotor position by installing a position sensor at the shaft end of the motor to achieve closed-loop control. However, the introduction of the position sensor increases the system cost and complicates the motor structure, and imposes higher requirements on installation precision and use environment.

[0003] To reduce system cost and simplify structure, existing technologies propose a sensorless control method for permanent magnet synchronous motors, i.e., estimating the rotor position and speed by algorithm without using a mechanical position sensor. Among them, a commonly used sensorless control scheme consists of a sliding mode observer and a phase-locked loop. The sliding mode observer is used to obtain the extended back electromotive force of the motor, and the phase-locked loop is used to calculate the electrical angle and speed of the motor from the extended back electromotive force.

[0004] However, due to its inherent control characteristics, the existing sliding mode observer usually contains strong high-frequency chattering components in the control quantity, and the extended back electromotive force is usually directly extracted from the control quantity, resulting in the extended back electromotive force also containing large chattering components. To suppress this chattering, a strong filtering process is often introduced, but this will cause phase delay and noise amplification problems, affecting the calculation accuracy of the phase-locked loop. In addition, in actual applications, the sliding mode observation algorithm needs to be discretized. When the sampling period is large or the motor inductance parameter is small, some coefficients in the discrete state equation may be too large, which can easily lead to the observation quantity being difficult to converge, and thus greatly reduce the stability and robustness of the sensorless control. SUMMARY

[0005] The present application provides a sliding mode observation method and system for sensorless control of a permanent magnet synchronous motor, for improving the numerical stability and observation accuracy of a discrete system, and realizing sensorless closed-loop control of a permanent magnet synchronous motor with good robustness and stability.

[0006] The first aspect of the present application provides a sliding mode observation method for sensorless control of a permanent magnet synchronous motor, comprising:

[0007] Obtaining actual current components and actual voltage components of the motor in a two-phase stationary coordinate system;

[0008] Establishing a current state equation based on the physical parameters of the motor, and discretizing the current state equation according to a sampling period to obtain an initial discrete sliding mode observation equation;

[0009] determining a proportional adjustment coefficient k based on the inductance parameter of the motor and the sampling period, weighting and adjusting the voltage-related term and the sliding mode control term in the initial discrete sliding mode observation equation by using the proportional adjustment coefficient k, and constructing an improved sliding mode observation equation, wherein the proportional adjustment coefficient k is a value greater than 0 and less than 1;

[0010] According to the improved sliding mode observation equation, the observed current at the current time is iteratively calculated based on the observed current at the previous time, the actual voltage component and the sliding mode control law, and the observed current is driven to track the actual current component;

[0011] extracting an extended back electromotive force based on the output of the sliding mode control law, wherein the contribution proportion of the high-frequency chattering component of the sliding mode control law output in the extended back electromotive force is weakened by the proportional adjustment coefficient;

[0012] inputting the extended back electromotive force into a phase-locked loop module to solve the rotor position and speed of the motor, and generating a control signal according to the rotor position and speed to realize closed-loop control of the motor.

[0013] Optionally, the determination of the proportional adjustment coefficient k based on the inductance parameter of the motor and the sampling period comprises:

[0014] calculating the ratio of the sampling period to the inductance parameter of the motor to obtain an initial weight coefficient corresponding to the sliding mode control in the discrete sliding mode observation equation;

[0015] determining whether the initial weight coefficient exceeds the numerical convergence boundary according to the influence of the initial weight coefficient on the observed current variation amplitude or the extended back electromotive force waveform in the discrete iterative calculation process;

[0016] If yes, a value greater than 0 and less than 1 is selected as the proportional adjustment coefficient k, and the initial weight coefficient is scaled by using the proportional adjustment coefficient k, so that the scaled weight coefficient is constrained within the numerical convergence boundary.

[0017] Optionally, after the value greater than 0 and less than 1 is selected as the proportional adjustment coefficient k and the initial weight coefficient is scaled by using the proportional adjustment coefficient k, the sliding mode observation method further comprises:

[0018] On the basis that the discrete sliding mode observation equation satisfies numerical convergence, the value of the proportional adjustment coefficient k is further reduced to reduce the high-frequency chattering component contained in the extended back electromotive force, until the amplitude of the high-frequency chattering component is lower than a preset signal noise tolerance threshold.

[0019] Optionally, the extraction of the extended back electromotive force based on the output of the sliding mode control law comprises:

[0020] Obtain the real-time output value of the sliding mode control law in the improved sliding mode observation equation, as well as the actual voltage components in the two-phase stationary coordinate system;

[0021] The extended back electromotive force is calculated by weighting and summing the actual voltage component and the real-time output value of the sliding mode control law using the proportional adjustment coefficient k, so that the main amplitude of the extended back electromotive force is provided by the actual voltage component without chattering.

[0022] The formula for the weighted summation calculation satisfies:

[0023] , ;

[0024] in, and They are respectively shaft and The extended back electromotive force of the shaft, , They are respectively shaft and The actual voltage component of the shaft, , This is the real-time output value of the sliding mode control law;

[0025] Before the extended back EMF input phase-locked loop module calculates the rotor position and speed of the motor, the sliding mode observation method further includes:

[0026] The extended back EMF is input to a first-order low-pass filter for filtering, and the cutoff frequency of the first-order low-pass filter is higher than the fundamental frequency of the motor.

[0027] Optionally, the initial discrete sliding mode observation equation is:

[0028] ;

[0029] in, , The sampling period is... For motor resistance, , These are the equivalent inductance parameters of the motor along the d-axis and q-axis in a synchronous rotating coordinate system, respectively. Electric angular velocity, , They are respectively shaft and The actual voltage component of the shaft, , They are respectively shaft and The observed current of the axis, , is the real-time output value of the sliding mode control law, and n is used to represent the discrete time step index.

[0030] Optionally, the improved sliding mode observation equation is:

[0031] ;

[0032] in, k is the proportional adjustment coefficient. The sampling period is... For motor resistance, , These are the equivalent inductance parameters of the motor along the d-axis and q-axis in a synchronous rotating coordinate system, respectively. Electric angular velocity, , They are respectively shaft and The actual voltage component of the shaft, , They are respectively shaft and The observed current of the axis, , is the real-time output value of the sliding mode control law, and n is used to represent the discrete time step index.

[0033] Optionally, the step of calculating the rotor position and speed of the motor by inputting the extended back EMF into the phase-locked loop module, and generating a control signal accordingly to achieve closed-loop control of the motor, includes:

[0034] The angle error signal characterizing the rotor position estimation error is calculated based on the extended back electromotive force;

[0035] The angle error signal is input into the proportional-integral controller, and the steady-state error is eliminated by the integral action of the proportional-integral controller to obtain the estimated speed of the motor.

[0036] The rotor position electrical angle of the motor is obtained by performing discrete integration on the estimated rotational speed value.

[0037] By using the rotor position electrical angle, a rotating coordinate transformation is performed on the actual current components in the two-phase stationary coordinate system to obtain the current feedback value in the synchronous rotating coordinate system.

[0038] Based on the difference between the preset current command value and the current feedback value, the voltage command value in the synchronous rotating coordinate system is calculated by the current loop controller;

[0039] The rotor position electrical angle is used to perform inverse coordinate transformation and space vector pulse width modulation operation on the voltage command value, to generate a PWM control signal for driving the motor.

[0040] Optionally, the sliding mode control law adopts a saturation function, a sign function, a continuous function or a hyperbolic tangent function.

[0041] The second aspect of the application provides a sliding mode observation system for permanent magnet synchronous motor sensorless control, comprising:

[0042] An acquisition unit is configured to acquire actual current components and actual voltage components of a motor in a two-phase stationary coordinate system;

[0043] A discretization unit is configured to establish a current state equation based on physical parameters of the motor, and to discretize the current state equation according to a sampling period to obtain an initial discrete sliding mode observation equation;

[0044] A construction unit is configured to determine a proportional adjustment coefficient k based on inductance parameters of the motor and the sampling period, to weight and adjust voltage-related terms and sliding mode control terms in the initial discrete sliding mode observation equation by using the proportional adjustment coefficient k, and to construct an improved sliding mode observation equation;

[0045] A calculation unit is configured to iteratively calculate an observation current at a current time based on an observation current at a previous time, the actual voltage components and a sliding mode control law according to the improved sliding mode observation equation, and to drive the observation current to track the actual current components;

[0046] An extraction unit is configured to extract an extended back electromotive force based on an output of the sliding mode control law, wherein a contribution proportion of a high-frequency chattering component of the output of the sliding mode control law in the extended back electromotive force is weakened by the proportional adjustment coefficient;

[0047] A control unit is configured to input the extended back electromotive force into a phase-locked loop module to calculate a rotor position and a speed of the motor, and to generate a control signal based on the rotor position and the speed to realize closed-loop control of the motor.

[0048] The third aspect of the application provides a sliding mode observation device for permanent magnet synchronous motor sensorless control, comprising:

[0049] A processor, a memory, an input / output unit and a bus;

[0050] The processor is connected with the memory, the input / output unit and the bus;

[0051] The memory stores a program, and the processor invokes the program to execute the first aspect and any optional sliding mode observation method for permanent magnet synchronous motor sensorless control in the first aspect.

[0052] The fourth aspect of the application provides a computer readable storage medium, which has a program stored thereon, and the program performs the first aspect and the sliding mode observation method for the non-inductive control of the permanent magnet synchronous motor in any one of the optional aspects of the first aspect when executed on a computer.

[0053] From the above technical solutions, the application has the following advantages:

[0054] Since the stability analysis of the sliding mode observer is usually based on a continuous system model, and the actual control system needs to be discretely implemented, when the sampling period is large or the motor inductance parameter is small, the numerical behavior of the discrete system is easy to deviate from the continuous system assumption, which is manifested as non-convergent observation or significant high-frequency chattering. Based on this, the application introduces a proportional adjustment coefficient k determined according to the sampling period and the motor inductance parameter on the basis of the discretization of the current state equation, and cooperatively adjusts the voltage-related term and the sliding mode control term in the discrete sliding mode observer, so as to effectively constrain the change amplitude in the unit sampling period in the discrete state update process without changing the sampling period, so that the discrete sliding mode observer is closer to the stability characteristics under the continuous system condition in the numerical behavior. Under this cooperative adjustment mechanism, the proportional adjustment coefficient on the one hand suppresses the high-frequency chattering amplitude introduced by the sliding mode control law in the discrete implementation process, and on the other hand synchronously reduces the proportion of the high-frequency chattering component in the extended back electromotive force, so that the phase-locked loop input signal constructed based on the extended back electromotive force is more smooth and stable, thereby improving the noise resistance and reliability of the rotor position and speed solving process, and avoiding the amplification and transmission of the observation chattering to the closed-loop control link.

[0055] By introducing the proportional adjustment coefficient on the basis of the discretization of the current state equation, the application cooperatively adjusts the voltage-related term and the sliding mode control term in the discrete sliding mode observer, so that the numerical stability and observation accuracy of the discrete system can be significantly improved without additional increase in computational complexity and without relying on shortening the sampling period, thereby realizing the non-inductive closed-loop control of the permanent magnet synchronous motor with good robustness and stability. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0057] Figure 1 An embodiment flowchart of the sliding mode observation method for the non-inductive control of the permanent magnet synchronous motor provided by the application is shown in the figure.

[0058] Figure 2 An embodiment flowchart for determining the proportional adjustment coefficient k in the sliding mode observation method for permanent magnet synchronous motor zero inductance control provided by the present application is shown in the figure below.

[0059] Figure 3 Another embodiment flowchart for the sliding mode observation method for permanent magnet synchronous motor zero inductance control provided by the present application is shown in the figure below.

[0060] Figure 4-a For Actual shaft current & tracking of conventional sliding mode observer Shaft current

[0061] Figure 4-b Observed by conventional sliding mode observer ;

[0062] Figure 5-a For Actual shaft current & tracking of improved sliding mode observer of the present application Shaft current

[0063] Figure 5-b Observed by improved sliding mode observer ;

[0064] Figure 6-a Actual electrical angle & observed electrical angle

[0065] Figure 6-b Observed electrical angle error

[0066] Figure 6-c Actual speed & observed speed

[0067] Figure 6-d Observed speed tracking effect at 50Hz

[0068] Figure 7 An embodiment structure diagram of the sliding mode observation system for permanent magnet synchronous motor zero inductance control provided by the present application is shown in the figure below.

[0069] Figure 8 An embodiment structure diagram of the sliding mode observation device for permanent magnet synchronous motor zero inductance control provided by the present application is shown in the figure below. DETAILED DESCRIPTION

[0070] The present application provides a sliding mode observation method and system for permanent magnet synchronous motor zero inductance control, which is used to improve the numerical stability and observation accuracy of discrete systems, and to realize permanent magnet synchronous motor zero inductance closed-loop control with good robustness and stability.

[0071] It should be noted that the method for slip mode observation for permanent magnet synchronous motor non-inductive control provided in the present application can be executed by various control devices. For example, the method can be directly integrated into a servo driver or controller of the permanent magnet synchronous motor as a firmware program, and executed by a microcontroller or digital signal processor inside the controller, so as to realize self-calibration and non-inductive control closed loop operation of the motor. The method can also be executed by an external host computer, programmable logic controller or special production line control device. In this case, the external device can issue real-time or constant speed operation instructions to the motor driver through a communication interface, and obtain current, voltage and other operation data from the driver, and complete iterative calculation and model processing on the external device to obtain the proportional adjustment coefficient k or the observed value of the extended back electromotive force, and then issue the calculation result to the driver or controller for real-time non-inductive control. For the convenience of description, the embodiments of the present application are described based on the controller, but this example does not constitute a limitation on the protection scope of the present application.

[0072] Please refer to Figure 1 , Figure 1 For one embodiment of the method for slip mode observation for permanent magnet synchronous motor non-inductive control provided in the present application, the method comprises:

[0073] 101, obtaining actual current components and actual voltage components of the motor in a two-phase static coordinate system;

[0074] To realize non-inductive control of the permanent magnet synchronous motor, the controller first needs to collect three-phase current signals and bus voltage signals of the motor, wherein the three-phase current refers to three-phase currents of the motor stator winding, which collectively reflect the instantaneous working state of the motor, and the bus voltage is a direct current voltage provided to the motor for controlling the torque output of the motor. Thereafter, the controller performs coordinate transformation on these signals, maps the three-phase static current and voltage to a two-phase static coordinate system to form intuitive observation quantities independent of the rotating magnetic field of the motor.

[0075] The two-phase static coordinate system, i.e. the alpha-beta coordinate system, is a physical model in which the three-phase stator winding of the motor is distributed 120 degrees apart in space, which is mathematically transformed into a mathematical model of two virtual axes perpendicular to each other and static, the purpose of which is to eliminate time-varying parameters and simplify the mathematical operation difficulty of the control system. The actual current components and actual voltage components refer to vector components that truly reflect the current electromagnetic state of the motor on the mathematical level after the analog signals collected by the physical sensor are transformed by the above coordinate system. In actual application, this conversion can be realized by Clarke transformation, which maps the three-phase current and voltage into two sets of perpendicular components through fixed linear combination, so that the subsequent observer does not need to rely on the rotor position sensor, and directly uses the two sets of signals for state estimation.

[0076] 102. Establish a current state equation based on the physical parameters of the motor, and discretize the current state equation according to the sampling period to obtain an initial discrete sliding mode observation equation;

[0077] After obtaining the actual current component and the actual voltage component of the motor operation, in order to realize the estimation of the internal state of the motor, a mathematical model capable of describing the electromagnetic behavior of the motor, i.e. the current state equation, needs to be constructed. The current state equation is constructed based on the inherent physical parameters of the motor, mainly including the stator resistance and the stator inductance, which objectively determine the current response dynamics of the motor under a specific voltage excitation. Since the actual control system of the motor uses a digital controller for calculation, the operation processing is based on discrete time steps, and it is impossible to directly solve the differential equation of continuous time, therefore, a sampling period, i.e. the operation period of the controller, is introduced as the time reference, and the continuous current state equation is discretized and converted.

[0078] In specific implementation, numerical analysis methods such as forward Euler method can be used to convert the differential items in the current state equation into difference form suitable for digital processor operation, or backward Euler, bilinear transformation and other methods can be used for discretization, which are not limited here. After this processing, an initial discrete sliding mode observation equation is obtained, which describes how to use the current value at the last time and the voltage value at the current time to recursively predict the current value at the next time.

[0079] 103. Determine a proportional adjustment coefficient k based on the inductance parameter of the motor and the sampling period, and use the proportional adjustment coefficient k to weight and adjust the voltage related items and the sliding mode control items in the initial discrete sliding mode observation equation to construct an improved sliding mode observation equation, the proportional adjustment coefficient k being a value greater than 0 and less than 1;

[0080] After obtaining the initial discrete sliding mode observation equation, in order to solve the problem that the traditional sliding mode observer is prone to divergence or high frequency chattering under actual operating conditions, a key proportional adjustment coefficient k is introduced in step 103. In a digital control system, the iteration step of the current observer is mainly determined by two physical quantities: one is the sampling period of the controller, which limits the time interval of discrete calculation; the other is the inductance parameter of the motor, which reflects the inertial response of current to voltage change. In actual engineering, if a low inductance motor is used or the sampling period is large due to hardware performance limitations, the coefficient before the control quantity in the discrete equation, i.e. the ratio of the sampling period to the inductance parameter, may increase significantly. In this case, the iteration step exceeds the stable region of numerical algorithm, which, from a physical point of view, means that the observed current produces significant overshoot and oscillation in each iteration, resulting in the observed value failing to converge to the true current, affecting the accuracy and stability of the non-inductive control.

[0081] To address this issue, this embodiment introduces a proportional adjustment coefficient k into both the voltage-related term reflecting the feedforward effect and the sliding mode control term reflecting the feedback correction effect in the initial discrete sliding mode observation equation. This coefficient k is then weighted and adjusted, with the value of k ranging from 0 to 1. This weighted adjustment essentially introduces a damping element at the input of the mathematical model, effectively limiting the current variation during iterative calculations and thus reshaping the dynamic characteristics of the observer. This improvement effectively controls the gain amplitude of a single iteration, maintains the phase response characteristics of the observer, and confines the closed-loop poles of the system to a stable region within the unit circle, thereby significantly improving numerical convergence and chattering resistance under digital discrete conditions.

[0082] 104. Based on the improved sliding mode observation equation, the observed current at the current moment is iteratively calculated based on the observed current, actual voltage components and sliding mode control law of the previous moment, and the observed current is driven to track the actual current components.

[0083] After obtaining the improved sliding mode observation equation, the controller iteratively calculates the current observed current based on the observed current from the previous moment, the actual voltage components in the two-phase stationary coordinate system, and the output of the sliding mode control law. This sliding mode control law is a nonlinear control algorithm used to incorporate the observer's error feedback into the observation equation, enabling rapid tracking of the actual motor current. Specifically, once a deviation is detected, the sliding mode control law immediately generates a virtual control quantity with a reverse correction function to correct the deviation. In each iteration of the practical application, the controller combines the observed current from the previous moment with the real-time acquired voltage signal and calculates the current observed current value using the improved sliding mode observation equation. The controller then uses this observed current as an estimation result to drive the output signal of the sliding mode observation equation to track the actual current components, achieving closed-loop control of the motor current.

[0084] 105. The extended back electromotive force is extracted based on the output of the sliding mode control law, wherein the contribution ratio of the high-frequency chattering component of the sliding mode control law output to the extended back electromotive force is weakened by the proportional adjustment coefficient.

[0085] After obtaining the observed current at the current moment, the controller can further extract the extended back electromotive force (EMF) using the output signal of the sliding mode control law. The extended back EMF is a key physical quantity used in sensorless motor control to estimate rotor position and speed; it reflects the instantaneous changes in the electromagnetic induction within the motor. In actual operation, the signal output by the sliding mode control law contains two parts: one is the effective correction to the actual current deviation of the motor, and the other is the high-frequency chattering component introduced by discrete iteration and control nonlinearity. If the original sliding mode control law output is directly used as the extended back EMF, the high-frequency chattering will be superimposed on the estimated signal, causing noise when the phase-locked loop calculates the rotor angle, affecting the stability and accuracy of the sensorless control.

[0086] Based on this, this embodiment utilizes the aforementioned proportional adjustment coefficient k to attenuate and weaken the high-frequency chattering component in the sliding mode control law output. Specifically, when generating the extended back EMF, the sliding mode control law output is scaled according to k. Since k is a value greater than 0 and less than 1, this operation objectively attenuates the amplitude of the high-frequency chattering component in the control quantity, significantly reducing the proportion of chattering noise in the final extracted extended back EMF signal while maintaining the integrity of the effective current correction signal. In this way, the extended back EMF can effectively suppress numerical noise generated during discrete iteration while preserving rotor position information and dynamic response characteristics.

[0087] 106. The extended back EMF input phase-locked loop module calculates the rotor position and speed of the motor, and generates control signals accordingly to achieve closed-loop control of the motor.

[0088] The controller inputs the extended back EMF to the phase-locked loop (PLL) module to calculate the rotor position and speed. This PLL module continuously tracks the phase of the extended back EMF vector. Specifically, the PLL calculates the phase error signal between the extended back EMF vector phase and the internally estimated phase based on arctangent calculation or a small-angle approximation algorithm. The phase error is then adjusted by a closed-loop regulator to update the speed estimate. Subsequently, the speed estimate is integrated to obtain the electrical angle corresponding to the rotor position. Because the high-frequency chattering component in the sliding mode control law output has been effectively weakened by the proportional adjustment coefficient k in the preceding steps, the extended back EMF signal input to the PLL module has high smoothness and a high signal-to-noise ratio, enabling the PLL to achieve stable phase locking without relying on a large time constant filtering stage. This signal characteristic improves the dynamic response capability of the PLL and reduces noise interference during phase estimation, thereby enhancing the accuracy of rotor position and speed calculations.

[0089] The controller feeds back the calculated rotor position electrical angle to the coordinate transformation stage of the vector control system. This transforms the acquired stator current from a two-phase stationary coordinate system to a synchronous rotating coordinate system, thereby achieving decoupled control of the torque and flux components. Simultaneously, the calculated rotational speed is used as feedback for the speed loop in closed-loop regulation. Based on this, the controller calculates the stator voltage command through the current and speed loops and generates a corresponding pulse width modulation signal using space vector pulse width modulation technology. This signal drives the inverter to apply control voltage to the permanent magnet synchronous motor, thus achieving sensorless closed-loop vector control operation.

[0090] In the embodiment, since the stability analysis of the sliding mode observer is generally based on a continuous system model, and the actual control system needs to be discretely implemented, when the sampling period is large or the motor inductance parameter is small, the numerical behavior of the discrete system is easy to deviate from the continuous system assumption, which is manifested as non-convergent observation or significant high-frequency chattering. Based on this, the application introduces a proportional adjustment coefficient k determined according to the sampling period and the motor inductance parameter on the basis of the discretization of the current state equation, and cooperatively adjusts the voltage-related term and the sliding mode control term in the discrete sliding mode observer, so as to effectively constrain the change amplitude in the unit sampling period in the discrete state update process without changing the sampling period, so that the discrete sliding mode observer is closer to the stability characteristics under the continuous system condition in the numerical behavior. Under this cooperative adjustment mechanism, the proportional adjustment coefficient suppresses the high-frequency chattering amplitude introduced by the sliding mode control law in the discrete implementation process, and simultaneously reduces the proportion of the high-frequency chattering component in the extended back electromotive force, so that the phase-locked loop input signal constructed based on the extended back electromotive force is more smooth and stable, thereby improving the noise resistance and reliability of the rotor position and speed solving process, and avoiding the amplification and transmission of the observation chattering to the closed-loop control link.

[0091] By introducing the proportional adjustment coefficient on the basis of the discretization of the current state equation, the application cooperatively adjusts the voltage-related term and the sliding mode control term in the discrete sliding mode observer, so that the numerical stability and observation accuracy of the discrete system can be significantly improved without additional calculation complexity and without relying on shortening the sampling period, thereby realizing the inductance-free closed-loop control of the permanent magnet synchronous motor with good robustness and stability.

[0092] Referring to Figure 2 According to some embodiments of the application, the proportional adjustment coefficient k is determined based on the inductance parameter and the sampling period of the motor in step S103, which can specifically include, but is not limited to, the following:

[0093] 201, calculating the ratio of the sampling period and the inductance parameter of the motor to obtain an initial weight coefficient corresponding to the sliding mode control quantity in the discrete sliding mode observation equation;

[0094] The controller calculates the ratio of the currently adopted sampling period and the inductance parameter of the motor to obtain an initial weight coefficient corresponding to the sliding mode control quantity in the discrete sliding mode observation equation. The initial weight coefficient reflects the influence intensity of the voltage action on the current change in the unit sampling period, and its numerical size directly determines the change amplitude of the observed current in each discrete iteration. In actual engineering, when the sampling period is relatively large or the motor inductance is small, the initial weight coefficient is often increased, thereby significantly amplifying the current change in a single iteration.

[0095] 202. judging whether the initial weight coefficient exceeds the numerical convergence boundary according to the influence of the initial weight coefficient on the amplitude of the observed current variation or the extended back-EMF waveform in the discrete iterative calculation process;

[0096] After obtaining the initial weight coefficient, it is necessary to evaluate its stability. In the iterative mechanism of the discrete sliding mode observer, there is a theoretical upper limit of the gain that maintains numerical convergence, i.e., the numerical convergence boundary in this application. If the initial weight coefficient is too large and exceeds this boundary, it means that the correction amplitude of the control quantity in a single sampling period exceeds the physical limit or mathematical stable domain of the system dynamic response. Such an excessively large gain is manifested in the actual physical phenomenon as a dramatic amplitude jump and high-frequency oscillation of the observed current in the iterative process, which cannot smoothly track the actual current; or as serious distortion and discontinuity in the extracted extended back-EMF waveform. Based on this, according to the stability criterion of discrete systems in control theory, by analyzing the system dynamic response that the coefficient may cause, such as whether it causes divergence or excessive chattering, it can be judged whether the current initial weight coefficient is within the stable and usable range.

[0097] 203. If yes, a value greater than 0 and less than 1 is selected as the proportional adjustment coefficient k, and the initial weight coefficient is scaled using the proportional adjustment coefficient k, so that the scaled weight coefficient is constrained within the numerical convergence boundary;

[0098] When the judgment result shows that the initial weight coefficient exceeds the numerical convergence boundary, it indicates that the current hardware conditions cannot directly support the stable operation of the traditional sliding mode observer, such as limited sampling frequency or small motor inductance, at this time a positive number between 0 and 1 can be selected as the proportional adjustment coefficient k. The controller uses this coefficient k to perform multiplication scaling operation on the excessively large initial weight coefficient to calculate the modified effective weight coefficient. The physical nature of this operation is to reduce the feedback gain in the discrete iterative process, and to forcibly pull the overflowed weight coefficient back within the numerical convergence boundary. Through this active constraint at the software level, it is ensured that the improved observer mathematical model meets the stability conditions of the discrete system, so that the system can avoid numerical divergence even in unfavorable hardware conditions, and realize the stable convergence of the observed current.

[0099] 204. On the basis of the discrete sliding mode observation equation satisfying numerical convergence, further reduce the value of the proportional adjustment coefficient k to reduce the high-frequency chattering component contained in the extended back-EMF, until the amplitude of the high-frequency chattering component is lower than the preset signal noise tolerance threshold.

[0100] After ensuring that the sliding mode observer meets the numerical convergence condition by initially setting the proportional adjustment coefficient k, although the risk of divergence can be eliminated and the current can be stably tracked, the high-frequency switching characteristics inherent in the sliding mode control will inevitably introduce high-frequency chattering in the control quantity. In order to further improve the observation accuracy, the secondary optimization of the proportional adjustment coefficient k can also be performed under the premise of meeting the stability constraint. Since in the improved observation model, the extended back electromotive force is synthesized by the actual voltage component and the sliding mode control quantity weighted by k, the size of the coefficient k directly determines the proportion of the high-frequency noise in the control quantity that is transmitted to the final back electromotive force result. Therefore, the controller can adopt the strategy of further reducing the numerical value of the proportional adjustment coefficient k, and use a smaller weight coefficient to physically suppress the high-frequency chattering component in the sliding mode control law output. The adjustment process aims to optimize the signal quality, and by reducing the value of k, the amplitude of the residual high-frequency chattering component in the extended back electromotive force is continuously attenuated until it is lower than the preset signal noise tolerance threshold. This strategy, on the basis of ensuring the stability of the system, significantly improves the signal-to-noise ratio of the observation signal by sacrificing a small amount of dynamic gain, thereby providing a noise smaller input source for subsequent phase-locked loop calculation.

[0101] The sliding mode observation method for sensorless control of permanent magnet synchronous motor provided by the present application will be described in detail below in combination with specific formulas and principles. Please refer to Figure 3 , Figure 3 Another embodiment of the sliding mode observation method for sensorless control of permanent magnet synchronous motor provided by the present application is provided, which comprises:

[0102] 301. Obtain the actual current component and the actual voltage component of the motor in the two-phase stationary coordinate system;

[0103] In this embodiment, step 301 is similar to step 101 of the previous embodiment, which will not be described here.

[0104] 302. Establish a current state equation based on the physical parameters of the motor, and discretize the current state equation according to the sampling period to obtain an initial discrete sliding mode observation equation;

[0105] The current state equation of the permanent magnet synchronous motor is as follows:

[0106] wherein, ,

[0107] In the formula, is the motor resistance, , are the d-axis and q-axis inductances, is the electrical angular velocity, , , , are respectively shaft shaft current voltage and is the EMF to be observed.

[0108] To this end, the conventional sliding mode observer is designed as follows:

[0109] wherein , is the observed current value, , is the control quantity. Its control law is , wherein G is a gain, is a sign function, which can be replaced by a saturation function, a continuous function or a hyperbolic tangent function, etc. as needed, and the specific function is not limited here.

[0110] When the observed current follows the actual current, there is = , = , thereby obtaining , values.

[0111] In this embodiment, the observer needs to be discretized into the following recursive form to obtain the initial discrete sliding mode observation equation:

[0112] ,

[0113] wherein , is a sampling period, i.e. an algorithm operation period, is a motor resistance, , are equivalent inductance parameters representing the d-axis and q-axis of the motor in the synchronous rotating coordinate system, is an electrical angular velocity, , are actual voltage components of the axis and the axis, , are observed currents of the axis and the axis, , is a real-time output value of the sliding mode control law, and n is used to represent a discrete time step index.

[0114] As can be seen from the discrete form, if the operation period is too large or the motor inductance is too small, the coefficient If the value is too large, it amplifies the chattering in the control variable, leading to drastic changes in the observed current and difficulty in convergence. Even optimizations such as changing the sign function in the control law to a saturation function can only alleviate chattering to a certain extent. Furthermore, because... = , = This introduces the chattering of the control variables entirely into the EMF, resulting in significant chattering within the EMF itself, which complicates the subsequent phase-locked loop (PLL) algorithm. This is the problem with traditional sliding mode observers.

[0115] 303. Based on the inductance parameters of the motor and the sampling period, determine the proportional adjustment coefficient k, and use the proportional adjustment coefficient k to perform weighted adjustment on the voltage-related terms and sliding mode control terms in the initial discrete sliding mode observation equation to construct the improved sliding mode observation equation.

[0116] As can be seen from the discrete form in step 302, if the operation period Too large or motor inductance Too small a value will lead to a decrease in the coefficient before the control quantity. If the value is too large, it amplifies the chattering in the control variable, leading to drastic changes in the observed current and difficulty in convergence. Even optimizations such as changing the sign function in the control law to a saturation function can only alleviate chattering to a certain extent. Furthermore, because... = , = This introduces the chattering of the control variables entirely into the EMF, resulting in significant chattering within the EMF itself, which complicates the subsequent phase-locked loop (PLL) algorithm. This is the problem with traditional sliding mode observers.

[0117] To address the aforementioned issues, this embodiment first determines the proportional control coefficient k based on the motor's inductance parameters and the sampling period, and then constructs an improved sliding mode observation equation using k, where k is a decimal between 0 and 1, and the control law remains unchanged. The improved sliding mode observation equation is constructed as follows:

[0118] ;

[0119] As shown in the equation, this embodiment adds a proportional adjustment coefficient before the voltage-related term and the sliding mode control term. Used for adjustment, can be based on , Determine the actual value situation The value ensures that the overall coefficients are within an appropriate range, thus affecting... , There are no longer any restrictions on the range of values, and it can adapt to motors with any calculation cycle and any inductance.

[0120] 304、According to the improved sliding mode observer equation, the observed current at the current time is iteratively calculated based on the observed current at the last time, the actual voltage component and the sliding mode control law, and the observed current is driven to track the actual current component;

[0121] In this embodiment, step 304 is similar to step 104 of the foregoing embodiment, and will not be described here.

[0122] 305、Obtaining the real-time output value of the sliding mode control law in the improved sliding mode observer equation and the actual voltage component in the two-phase static coordinate system;

[0123] At the end of each control cycle of the iterative operation of the observer, the controller needs to read the real-time output value of the sliding mode control law at the current time from the improved sliding mode observer equation, i.e., the control variable 、 , which contains a high-frequency switching signal for correcting the error of the observed current. At the same time, the controller synchronously obtains the actual voltage component reconstructed in the two-phase static coordinate system, i.e., 、 . These two variables respectively represent the feedback correction term and the feedforward input term, which constitute the basic data for synthesizing the extended back electromotive force signal.

[0124] 306、Using the proportional adjustment coefficient k to weight and sum the actual voltage component and the real-time output value of the sliding mode control law to obtain the extended back electromotive force, so that the main amplitude of the extended back electromotive force is provided by the actual voltage component without chattering;

[0125] After obtaining the basic data, the controller performs signal synthesis operation based on the proportional adjustment coefficient k. Unlike the traditional sliding mode observer which directly equates the control variable containing severe noise to the back electromotive force, this embodiment is based on the mathematical structure of the improved sliding mode observer equation, and the actual voltage component and the output value of the sliding mode control law are weighted and summed, so that the main amplitude of the extended back electromotive force is provided by the actual voltage component without chattering. The formula of the weighted sum calculation satisfies:

[0126] , ;

[0127] Among them, and are the extended back electromotive forces of the axis and the axis, 、 are the actual voltage components of the axis and the axis, 、 This represents the real-time output value of the sliding mode control law. It can be seen that only a portion of the EMF value comes from control variables with chattering characteristics, and these variables have been modified... Scaling by a factor of 1, when When the value is small, this scaling is very significant, which greatly reduces the amount of jitter in the EMF, thereby obtaining an EMF value with very high signal quality.

[0128] Overall, the high-frequency chattering component originating from the sliding mode control law in the extended back EMF is significantly scaled and attenuated by a ratio k, while the fundamental component containing the true rotor information is fully preserved through the voltage component. This mechanism fundamentally alters the composition of the observed signal, resulting in a significantly reduced chattering noise level in the final extended back EMF while preserving dynamic information, thus enabling the acquisition of observations with extremely high signal quality.

[0129] 307. The extended back EMF input is filtered by a first-order low-pass filter. The cutoff frequency of the first-order low-pass filter is higher than the fundamental frequency of the motor.

[0130] After weighted calculation, the signal-to-noise ratio of the extended back EMF has been significantly improved. To further filter out residual quantization noise or subharmonics, the controller inputs the signal into a digital first-order low-pass filter for processing. Since the jitter amplitude has been effectively suppressed using the proportional adjustment coefficient k in the previous steps, the noise energy in the signal is now at an extremely low level. Therefore, it is no longer necessary to rely on a low-cutoff frequency filter to forcibly filter out noise. Thus, the cutoff frequency of this first-order low-pass filter can be configured to be higher than the fundamental frequency of the motor. This high cutoff frequency configuration strategy, while ensuring signal smoothness, greatly reduces the phase lag introduced by the filter, ensuring the phase accuracy of the extended back EMF signal, thereby avoiding rotor position estimation errors caused by filtering delay.

[0131] 308. Calculate the angle error signal characterizing the rotor position estimation error based on the extended back electromotive force;

[0132] After obtaining the filtered, high-quality extended back EMF, the controller enters the position calculation stage. Since the back EMF vector lags behind the rotor flux linkage vector by 90 degrees (or is orthogonal) in space, its two components contain the rotor's current absolute position information. The controller uses trigonometric function operations or small-angle approximation algorithms to process the extended back EMF components along the α and β axes, calculating the phase angle of the current extended back EMF vector. This phase angle is then compared with the rotor position angle estimated at the previous moment, or the back EMF is directly projected onto the d-axis of the estimated rotating coordinate system using a coordinate transformation based on the estimated angle, thus obtaining an angular error signal characterizing the deviation between the actual rotor position and the estimated position.

[0133] 309. Input the angle error signal into the proportional-integral controller. The steady-state error is eliminated by the integral action of the proportional-integral controller, and the estimated value of the motor speed is obtained.

[0134] To achieve zero steady-state error tracking of the rotor position, the controller employs a software phase-locked loop (PLL) structure. The calculated angle error signal is input to the proportional-integral (PI) regulator within the PLL. In this closed-loop regulation, the proportional element responds to rapidly changing phase errors, providing the system with dynamic tracking capability; while the integral element accumulates historical errors, using its zero steady-state error characteristic to force the steady-state angle error to approach zero. As the regulation process progresses, the PI regulator's output is continuously corrected, eventually converging to a stable value. This value physically corresponds to the motor's current electrical angular velocity, i.e., the estimated motor speed.

[0135] 310. Perform discrete integration on the estimated rotational speed to obtain the electrical angle of the motor rotor position;

[0136] After obtaining the converged speed estimate, the controller performs integration in the discrete-time domain. Specifically, the controller multiplies the current speed estimate by the sampling period and adds it to the rotor position angle from the previous moment to recursively obtain the current rotor position electrical angle. This electrical angle is a key variable for achieving vector control orientation, and its accuracy directly determines the smoothness and efficiency of the motor torque output. The integration process typically includes angle normalization, constraining the angle value within a period range of 0 to 2π to meet the continuous rotation control requirements of the motor.

[0137] 311. Using the rotor position electrical angle, perform a rotating coordinate transformation on the actual current components in the two-phase stationary coordinate system to obtain the current feedback value in the synchronous rotating coordinate system.

[0138] The controller performs a rotating coordinate transformation on the actual current components in the two-phase stationary coordinate system obtained in step 301. This mathematical transformation uses a trigonometric function matrix to project the AC current signal in the stationary coordinate system onto the dq coordinate system, which rotates synchronously with the rotor, converting it into a DC current feedback value. At this point, the d-axis current component corresponds to the excitation component, and the q-axis current component corresponds to the torque component, thus achieving linearization and decoupling of the AC motor control object.

[0139] 312. Based on the difference between the preset current command value and the current feedback value, the voltage command value in the synchronous rotating coordinate system is calculated by the current loop controller;

[0140] In the synchronous rotating coordinate system, the controller performs subtraction operation between the preset current command value (usually the d-axis command is 0 to achieve maximum torque current ratio control, and the q-axis command is determined by the speed loop output) and the actual current feedback value obtained in step 311, to obtain a current tracking error. The error is sent to the current loop PI controller for adjustment. According to the size and direction of the error, the current loop controller calculates the control voltage vector required to eliminate the error, that is, the d-axis voltage command value and the q-axis voltage command value in the synchronous rotating coordinate system.

[0141] 313. Perform inverse coordinate transformation and space vector pulse width modulation operation on the voltage command value using the rotor position electrical angle to generate a PWM control signal for driving the motor.

[0142] In order to drive the three-phase inverter to act, the controller needs to restore the direct current voltage command in the rotating coordinate system to the control signal in the stationary coordinate system. Using the rotor position electrical angle obtained in step 310, the controller performs inverse Park transformation on the d-axis and q-axis voltage command values to obtain the voltage vector in the two-phase stationary coordinate system, that is, 、 Subsequently, the SVPWM algorithm calculates the conduction time of each bridge arm power switch device of the inverter according to the sector and amplitude of the voltage vector, and generates the corresponding PWM control signal. These PWM signals finally act on the power drive circuit to control the motor stator winding voltage, form a circular rotating magnetic field to drive the motor to operate, thereby completing the complete process of inductiveless control from current sampling, state observation to closed-loop driving.

[0143] The improved sliding mode observer and the traditional sliding mode observer in the sliding mode observation method for inductiveless control of permanent magnet synchronous motor provided in the present application are compared in terms of stability conditions to verify that the introduction of the proportional regulation coefficient k does not adversely affect the stability of the system. It should be noted that both sliding mode observers are designed based on the same Lyapunov stability criterion , where The difference mainly lies in the relative weight relationship of each parameter in the stability inequality in the discrete implementation condition. Then the stability requirement is:

[0144] Traditional sliding mode observer:

[0145] ;

[0146] Improved sliding mode observer of the present application:

[0147] ;

[0148] The value of the sliding mode gain parameter G does not require accuracy, and only needs to be as small as possible under the premise of meeting the stability condition, so when comparing the stability requirements of the two observers, the dominant factor can be analyzed from the engineering application point of view. In practice, compared with the voltage term 、 The possible maximum value (about 2 / 3 of the bus voltage), 、 is much smaller (may be more than 2 orders of magnitude), so the dominant term is the voltage-related term, and combined with the motor voltage equation as follows

[0149] , wherein is a differential operator;

[0150] In practice, 、 are very small values, especially for surface-mounted permanent magnet synchronous motors, the difference between the parameters of each axis can be ignored, so the motor terminal voltage and the extended back electromotive force are in the same order of magnitude in amplitude and direction. Therefore, only by comparing the difference between the two stability requirements, it can be roughly considered from the voltage equation that = , = , that is, the motor voltage and the extended back electromotive force are in the same order of magnitude and have the same direction. Then , .

[0151] Therefore, the above stability condition is simplified as follows:

[0152] Traditional sliding mode observer:

[0153] ;

[0154] Improved sliding mode observer of the application:

[0155] ;

[0156] Although the application increases the front current term by times, as mentioned above, the value itself is much smaller than the latter term, so even if it is increased, it is still smaller than the latter voltage term, and the dominant term of the G value is still the voltage term. Therefore, it can be seen that the gain G required by the stability condition of the application is equivalent to that of the traditional sliding mode observer, and the G is not significantly increased due to the introduction of the adjustment factor k.

[0157] Next, further experiments are used to verify that in an actual resistance R = 45 , inductance = = 44 Comparative verification was performed on the joint motor, with the algorithm sampling period being T=50. All have a gain of G=30, and the control law is in saturation function form. Then the coefficients... = 1.136. For the traditional sliding mode algorithm (i.e., k=1), the coefficients are too large at this point, resulting in severe chattering during current tracking, making convergence difficult. The EMF observations jump back and forth between G values, rendering the results unusable. For example... Figure 4-a and Figure 4-b As shown, where Figure 4-a for Actual shaft current & traditional sliding mode observer tracking shaft current; Figure 4-b For observations by a traditional sliding mode observer .

[0158] For the improved sliding mode observer of this invention, when k=0.02, its current can track the actual current very well, the observed EMF curve is smooth, the jitter is very small, and the signal quality is very high. Figure 5-a and Figure 5-b As shown, where Figure 5-a for Actual shaft current & tracking of the improved sliding mode observer in this application shaft current; Figure 5-b For observations by the improved sliding mode observer .

[0159] Then, by slightly filtering the EMF value of this invention, for example, setting the filter cutoff frequency to 2300 rad / s in this experiment, it can be input into the next PLL stage to obtain the motor angle and speed. Figure 6-a , 6-b As shown in 6-c and 6-d, Figure 6-a For actual electrical angle & observed electrical angle; Figure 6-b The electrical angle error of the observation; Figure 6-c For actual speed and observed speed; Figure 6-d The velocity tracking effect is observed at 50Hz.

[0160] It can be seen that the observed electrical angle is almost coincident with the actual electrical angle (measured by the encoder which has been calibrated and delay compensated). The average absolute error of the observed mechanical angle (obtained by dividing the electrical angle by the number of pole pairs, which is 10 in this test) compared with the actual mechanical angle is only 0.025 degrees, and the maximum deviation is only 0.11 degrees. The observed speed is consistent with the actual speed, and the noise standard deviation is 0.78 rad / s, while the observed speed tracking bandwidth can reach more than 100 Hz. It is particularly important to note that this is only a simple data to illustrate the advantages of the sliding mode observer of the present application, and the sliding mode control law and the phase-locked loop have not been optimized. In actual application, the sliding mode control law and the phase-locked loop can also be deeply optimized, and the application effect will be better.

[0161] It has been fully shown by the above experiments that, under the same sampling period, the same sliding mode gain and the same control law form, the traditional sliding mode observer is prone to current chattering and unusable observation results under the condition of a low-inductance motor, while the improved sliding mode observer of the present application effectively suppresses numerical oscillation in the discrete iterative process by introducing a proportional adjustment coefficient k, so that the observed current can stably track the actual current, and the back-EMF waveform is smooth and reliable, thereby providing high-quality input for subsequent phase-locked loop calculation of rotor position and speed.

[0162] Finally, it should be noted that the present application only introduces a proportional adjustment coefficient k in the voltage-related term and the sliding mode control term in the discrete sliding mode observation equation to synchronously weight and adjust the related terms to achieve overall constraint on the discrete iterative characteristics of the observer. However, the core idea of the present application is not limited to using only a single adjustment factor or only acting on the above specific terms. Without deviating from the basic principles of the present application, the skilled person can also introduce one or more adjustment factors in other terms of the observation equation or use different adjustment factors to weight and adjust the terms corresponding to different physical quantities according to specific motor parameters, sampling period, control target or numerical stability requirements, so as to further refine the constraint and optimization of the dynamic characteristics of the discrete system. For example, independent adjustment coefficients can be set for the current state term, the resistance-related term or the coupling term, or the proportional adjustment coefficient can be extended to an adaptive parameter that changes with the operating state. The above modified schemes all belong to equivalent extension of the technical idea of the present application.

[0163] The sliding mode observation system for sensorless control of a permanent magnet synchronous motor provided by the present application will be described in detail below. Please refer to Figure 7 , Figure 7 An embodiment of the sliding mode observation system for sensorless control of a permanent magnet synchronous motor provided by the present application comprises:

[0164] The acquisition unit 701 is configured to acquire actual current components and actual voltage components of the motor in a two-phase stationary coordinate system.

[0165] The discrete unit 702 is configured to establish a current state equation based on a physical parameter of the motor, and discretize the current state equation according to a sampling period to obtain an initial discrete sliding mode observation equation;

[0166] The construction unit 703 is configured to determine a proportional adjustment coefficient k based on an inductance parameter of the motor and the sampling period, and adjust the voltage related term and the sliding mode control term in the initial discrete sliding mode observation equation by using the proportional adjustment coefficient k to construct an improved sliding mode observation equation;

[0167] The calculation unit 704 is configured to calculate an observed current at a current moment based on an observed current at a previous moment, an actual voltage component and a sliding mode control law according to the improved sliding mode observation equation, and drive the observed current to track the actual current component;

[0168] The extraction unit 705 is configured to extract an extended back electromotive force based on an output of the sliding mode control law, wherein a contribution proportion of a high frequency chattering component of the sliding mode control law output in the extended back electromotive force is weakened by the proportional adjustment coefficient;

[0169] The control unit 706 is configured to input the extended back electromotive force into a phase-locked loop module to calculate a rotor position and a speed of the motor, and generate a control signal based on the rotor position and the speed to realize closed-loop control of the motor.

[0170] In the system of the embodiment, the functions of the units correspond to the steps in the method embodiments described above, and will not be described here. Figures 1 to 3

[0171] The application further provides a sliding mode observation device for sensorless control of a permanent magnet synchronous motor, please refer to Figure 8 , Figure 8 An embodiment of the sliding mode observation device for sensorless control of a permanent magnet synchronous motor provided by the application, the device comprises:

[0172] The processor 801, the memory 802, the input and output unit 803, and the bus 804;

[0173] The processor 801 is connected with the memory 802, the input and output unit 803, and the bus 804;

[0174] The memory 802 stores a program, and the processor 801 calls the program to execute any one of the sliding mode observation methods for sensorless control of a permanent magnet synchronous motor.

[0175] The application further relates to a computer readable storage medium, and the computer readable storage medium stores a program, when the program runs on a computer, the computer executes any one of the sliding mode observation methods for sensorless control of a permanent magnet synchronous motor. ​

[0176] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0177] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0178] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0179] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present alone, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0180] If the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, read-only memory), a random access memory (RAM, random access memory), a magnetic disk or an optical disk, and various program code storage media.

Claims

1. A sliding mode observation method for sensorless control of permanent magnet synchronous machines, characterized in that, The sliding mode observation method comprises: acquiring actual current components and actual voltage components of the motor in a two-phase stationary coordinate system; establishing a current state equation based on physical parameters of the motor, and discretizing the current state equation according to a sampling period to obtain an initial discrete sliding mode observation equation; calculating a ratio of the sampling period to an inductance parameter of the motor to obtain an initial weight coefficient corresponding to a sliding mode control quantity in the discrete sliding mode observation equation; judging whether the initial weight coefficient exceeds a numerical convergence boundary according to an influence of the initial weight coefficient on an observation current variation amplitude or an extended back electromotive force waveform in a discrete iterative calculation process; if yes, selecting a numerical value greater than 0 and less than 1 as a proportional adjustment coefficient k, and scaling the initial weight coefficient by using the proportional adjustment coefficient k, so that the scaled weight coefficient is constrained within the numerical convergence boundary; weighting and adjusting voltage-related terms and sliding mode control quantity terms in the initial discrete sliding mode observation equation by using the proportional adjustment coefficient k to construct an improved sliding mode observation equation, the proportional adjustment coefficient k being a numerical value greater than 0 and less than 1; calculating an observation current at a current moment based on an observation current at a previous moment, the actual voltage components and a sliding mode control law according to the improved sliding mode observation equation, and driving the observation current to track the actual current components; extracting an extended back electromotive force based on an output of the sliding mode control law, wherein a contribution proportion of a high-frequency chattering component of the sliding mode control law output in the extended back electromotive force is weakened by the proportional adjustment coefficient; inputting the extended back electromotive force into a phase-locked loop module to solve a rotor position and a rotor speed of the motor, and generating a control signal according to the rotor position and the rotor speed to realize closed-loop control of the motor; after the proportional adjustment coefficient k is selected as a numerical value greater than 0 and less than 1, and the initial weight coefficient is scaled by using the proportional adjustment coefficient k, the sliding mode observation method further comprises: further reducing the numerical value of the proportional adjustment coefficient k to reduce the high-frequency chattering component contained in the extended back electromotive force on the basis that the discrete sliding mode observation equation satisfies numerical convergence, until an amplitude of the high-frequency chattering component is lower than a preset signal noise tolerance threshold.

2. The sliding mode observation method according to claim 1, characterized in that, The extracting of the extended back electromotive force based on the output of the sliding mode control law comprises: acquiring a real-time output value of the sliding mode control law in the improved sliding mode observation equation and the actual voltage components in the two-phase stationary coordinate system; performing weighted summation calculation on the actual voltage components and the real-time output value of the sliding mode control law by using the proportional adjustment coefficient k to obtain the extended back electromotive force, so that a main amplitude of the extended back electromotive force is provided by the actual voltage components without chattering; a formula of the weighted summation calculation satisfies: ， ； wherein, and are respectively the actual voltage components of the axes, , are respectively the actual voltage components of the axes, , is the real-time output value of the sliding mode control law; before the extended back electromotive force is input into the phase-locked loop module to solve the rotor position and the rotor speed of the motor, the sliding mode observation method further comprises: inputting the extended back electromotive force into a first-order low-pass filter for filtering processing, a cutoff frequency of the first-order low-pass filter being higher than a fundamental frequency of the motor.

3. The sliding mode observer method of claim 1, wherein The initial discrete sliding mode observation equation is: ； wherein, , is the sampling period, is the motor resistance, , are the equivalent inductance parameters representing the d-axis and q-axis of the motor in the synchronous rotating reference frame, respectively, is the electrical angular velocity, , are the actual voltage components of the -axis and -axis, respectively, , are the observed currents of the -axis and -axis, respectively, , is the real-time output value of the sliding mode control law, n is used to denote the discrete time step index.

4. The sliding mode observer method of claim 3, wherein The improved sliding mode observation equation is: ； in, k is the proportional adjustment coefficient. The sampling period is... For motor resistance, , These are the equivalent inductance parameters of the motor along the d-axis and q-axis in a synchronous rotating coordinate system, respectively. Electric angular velocity, , They are respectively shaft and The actual voltage component of the shaft, , They are respectively shaft and The observed current of the axis, , is the real-time output value of the sliding mode control law, and n is used to represent the discrete time step index.

5. The sliding mode observer method according to any one of claims 1 to 4, characterized in that, The control signal is generated according to the rotor position and the speed of the motor calculated by inputting the extended back electromotive force into a phase-locked loop module to realize closed-loop control of the motor, and the control signal comprises: An angle error signal representing an estimation error of the rotor position is calculated based on the extended back electromotive force; The angle error signal is input into a proportional-integral regulator, and a steady-state error is eliminated by integral action of the proportional-integral regulator to obtain an estimated value of the speed of the motor; A discrete integral operation is performed on the estimated value of the speed to obtain an electrical angle of the rotor position of the motor; A rotating coordinate transformation is performed on actual current components in the two-phase stationary coordinate system by using the electrical angle of the rotor position to obtain current feedback values in a synchronous rotating coordinate system; A voltage command value in the synchronous rotating coordinate system is calculated by a current loop controller according to a difference between a preset current command value and the current feedback values; An inverse coordinate transformation and a space vector pulse width modulation operation are performed on the voltage command value by using the electrical angle of the rotor position to generate a PWM control signal for driving the motor.

6. The sliding mode observer method according to any one of claims 1 to 4, characterized in that, The sliding mode control law adopts a saturation function, a sign function, a continuous function or a hyperbolic tangent function.

7. A sliding mode observer system for fluxless control of a permanent magnet synchronous motor, characterized in that, The sliding mode observation system is used to perform the sliding mode observation method of claim 1, and the sliding mode observation system comprises: An acquisition unit is configured to acquire actual current components and actual voltage components of a motor in a two-phase stationary coordinate system; A discrete unit is configured to establish a current state equation based on physical parameters of the motor, and to perform a discretization process on the current state equation according to a sampling period to obtain an initial discrete sliding mode observation equation; A construction unit is configured to determine a proportional adjustment coefficient k based on inductance parameters of the motor and the sampling period, to perform a weighting adjustment on voltage-related terms and sliding mode control terms in the initial discrete sliding mode observation equation by using the proportional adjustment coefficient k, and to construct an improved sliding mode observation equation; A calculation unit is configured to iteratively calculate an observed current at a current time based on an observed current at a previous time, the actual voltage components and a sliding mode control law according to the improved sliding mode observation equation, and to drive the observed current to track the actual current components; An extraction unit is configured to extract an extended back electromotive force based on an output of the sliding mode control law, wherein a contribution proportion of a high-frequency chattering component output by the sliding mode control law in the extended back electromotive force is weakened by the proportional adjustment coefficient; A control unit is configured to input the extended back electromotive force into a phase-locked loop module to calculate a rotor position and a speed of the motor, and to generate a control signal to realize closed-loop control of the motor.

8. A sliding mode observer device for fluxless control of permanent magnet synchronous machines, characterized in that, The sliding mode observation device comprises: A processor, a memory, an input-output unit and a bus; The processor is connected with the memory, the input-output unit and the bus; The memory stores a program, and the processor invokes the program to perform the method of any one of claims 1 to 6.

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