A method and system for suppressing multi-source nonlinear disturbance of permanent magnet synchronous motor

Abstract

The application relates to a permanent magnet synchronous motor multi-source nonlinear disturbance suppression method and system, and belongs to the technical field of permanent magnet synchronous motor control. The application comprises the following steps: establishing an electromechanical coupling discrete model of a permanent magnet synchronous motor actuator; designing a linear extended state observer based on a super-local model to observe system total disturbance in real time; decomposing the system total disturbance into multiple nonlinear disturbance factor components based on the electromechanical coupling discrete model, and calculating the contribution degrees of the components; designing a backstepping adaptive controller based on the contribution degrees, and combining the linear extended state observer to perform feedforward compensation; designing a deadbeat predictive current controller, and performing weighted fusion of the backstepping adaptive controller through a dynamic weight optimization strategy to generate a control voltage; and applying the finally generated d-axis and q-axis voltage control quantities to the permanent magnet synchronous motor actuator. The application can precisely suppress multi-source nonlinear disturbance, and improve the tracking accuracy, dynamic response speed and robustness of the system.

Description

Technical Field

[0001] This invention belongs to the field of permanent magnet synchronous motor control technology, and relates to a method and system for suppressing multi-source nonlinear disturbances in permanent magnet synchronous motors based on cascaded backstepping adaptive deadbeat predictive control. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs) are widely used in high-precision drive applications such as wind power, aerospace, electric vehicles, and industrial robots due to their high power density and efficiency. To achieve precise motion control, the current loop needs to possess fast dynamic response characteristics. However, the PMSM drive system is a typical nonlinear, strongly coupled system, and its control performance is significantly affected by various nonlinear factors, including complex internal nonlinear tribodynamics, mechanical transmission backlash, time-varying parameters, and external time-varying load disturbances. The presence of these multi-source nonlinear disturbances complicates controller design and makes it difficult to achieve ideal tracking performance.

[0003] Traditional permanent magnet synchronous motor (PMSM) control often employs vector control strategies based on PI controllers, using a cascaded speed and current loop system structure. While PI control is simple and reliable, its robustness is poor, and its performance degrades significantly when faced with parameter variations and external disturbances. To improve system performance, various advanced control strategies have emerged in recent years, such as active disturbance rejection control (ADRC), internal model control (IMC), sliding mode variable structure control (SMC), adaptive fuzzy control, and model predictive control.

[0004] However, existing methods typically simplify heterogeneous disturbance sources into a single total disturbance term, lacking quantitative modeling and analysis of the dynamic characteristics of the disturbance, thus limiting further improvements in control performance. Backstepping adaptive control possesses systematic design characteristics and can handle nonlinear systems, but its robustness to parameter uncertainties and external disturbances still needs improvement. Deadbeat predictive control offers fast dynamic response but is sensitive to model parameters and has limited ability to suppress nonlinearity and disturbances.

[0005] Therefore, there is an urgent need for a high-performance control method that can quantitatively model, observe in real time, and adaptively compensate for multi-source nonlinear disturbances, while taking into account both dynamic response speed and global stability. Summary of the Invention

[0006] In view of this, the purpose of this invention is to provide a method and system for suppressing multi-source nonlinear disturbances in permanent magnet synchronous motors. This method involves establishing an accurate electromechanical coupling nonlinear model, designing a linear extended state observer to estimate the total disturbance in real time, and quantifying the influence of each disturbance source based on a disturbance contribution mechanism. Furthermore, it combines backstepping adaptive control and deadbeat predictive control, achieving the cascaded fusion of these two control strategies through dynamic weight optimization. This significantly improves the system's tracking accuracy, dynamic response, and disturbance rejection capability under complex operating conditions while ensuring overall system stability.

[0007] To achieve the above objectives, the first aspect of the present invention provides a method for suppressing multi-source nonlinear disturbances in a permanent magnet synchronous motor, comprising: An electromechanical coupling discrete model of the permanent magnet synchronous motor actuator is established, including the equivalent dynamic model of the transmission system, the nonlinear disturbance model, and the electrical model of the motor. A linear extended state observer is designed based on a hyperlocal model to observe the total disturbance of the system in real time. Based on the established electromechanical coupling discrete model, the total disturbance of the system is decomposed into multiple nonlinear disturbance factor components, and the contribution of each component is calculated. A backstep adaptive controller is designed based on contribution, and feedforward compensation is performed in conjunction with a linearly extended state observer. Design a deadbeat predictive current controller and generate a control voltage by weighted fusion of a dynamic weight optimization strategy and a backstep adaptive controller. The final generated d-axis and q-axis voltage control values ​​are applied to the permanent magnet synchronous motor actuator.

[0008] The second aspect of the present invention provides a control system for implementing the method described in the first aspect, comprising a signal acquisition and processing unit, a disturbance observation and calculation unit, a backstepping adaptive control unit, a beatless prediction control unit, a dynamic optimization and fusion unit, and a modulation and driving unit.

[0009] A third aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0010] A fourth aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the stored computer program, it implements the method described in the first aspect.

[0011] The beneficial effects of this invention are as follows: (1) This invention establishes an electromechanical coupling discrete model containing nonlinear factors and designs a linear extended state observer to realize real-time estimation and quantitative decomposition of multi-source nonlinear disturbances, providing a basis for targeted compensation.

[0012] (2) This invention proposes a dynamic weight factor design method based on disturbance contribution, which enables the controller to adaptively emphasize the compensation of the main disturbance sources according to the operating conditions, thereby improving the intelligence and effectiveness of the control.

[0013] (3) This invention integrates backstep adaptive control and deadbeat predictive control through a dynamic weight optimization mechanism, combining the global robustness of backstep adaptive control with the fast dynamic response capability of deadbeat predictive control, and achieving a balance between performance and robustness under complex working conditions.

[0014] (4) The present invention has a clear structure and relatively simple parameter adjustment. Through experimental verification, it has shown excellent performance with no overshoot, fast response, small fluctuation and strong anti-interference ability in scenarios such as start-up, speed regulation and variable load. It has high engineering application value.

[0015] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0016] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a schematic diagram of the multi-source nonlinear disturbance suppression method for permanent magnet synchronous motors in Example 1; Figure 2 This is a schematic diagram of electromechanical coupling dynamics; Figure 3 For control logic block diagram; Figure 4 Schematic diagram of the test bench architecture; Figure 5 The diagram shows a comparison of the speed response of three schemes: LESO-BS, LESO-DPCC, and LESO-BS-DPCC. (a) represents LESO-BS, (b) represents LESO-DPCC, and (c) represents LESO-BS-DPCC. Figure 6 The diagrams show a comparison of the iq-axis current response of three schemes: LESO-BS, LESO-DPCC, and LESO-BS-DPCC. (a) represents LESO-BS, (b) represents LESO-DPCC, and (c) represents LESO-BS-DPCC. Figure 7 Let (a) be the rotational speed and iq-axis current of the LESO-BS, where (a) is the rotational speed and (b) is the iq-axis current. Figure 8 Let (a) be the rotational speed and iq-axis current of the LESO-DPCC, where (a) is the rotational speed and (b) is the iq-axis current. Figure 9Let (a) be the rotational speed and iq-axis current of the LESO-BS-DPCC, where (a) is the rotational speed and (b) is the iq-axis current. Figure 10 This is a comparative diagram of the A-phase current THD of the three schemes LESO-BS, LESO-DPCC, and LESO-BS-DPCC under various operating conditions. Detailed Implementation

[0017] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0018] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0019] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0020] Example 1 This embodiment provides a method for suppressing multi-source nonlinear disturbances in a permanent magnet synchronous motor, such as... Figure 1 As shown, it is described below: 1. System Modeling For surface-mounted permanent magnet synchronous motors (SPMSM), considering nonlinear factors such as backlash, friction, and error in the transmission system, an electromechanical coupling discrete model of the PMSM actuator is established, including the equivalent dynamic model of the transmission system, the nonlinear disturbance model, and the electrical model of the motor.

[0021] The equivalent dynamic model of the transmission system is expressed as follows: (1) In the formula, The equivalent rotational inertia of the system, Input shaft speed The derivative, For electromagnetic torque, For load torque, The equivalent transmission ratio includes fluctuations; The moment of inertia of the equivalent component at the input end. The moment of inertia of the equivalent component at the output end. For a constant transmission ratio, For transmission ratio fluctuation; Nonlinear perturbation models include gap errors Transmission error and LuGre friction model The gap error is approximated using a continuously differentiable function: (2) In the formula, For the angle of the sun gear, For time, For the input angle error, The gap width, The slope constant is... This is the gap damping coefficient. To account for the overall transmission speed error; The LuGre friction model is expressed as: (3) (4) In the formula, This is the stiffness coefficient. This is the pre-sliding damping coefficient. It is the coefficient of viscous friction; The average deformation of the bristles is unmeasurable. , and These represent Coulomb friction, static friction, and the Stribeck velocity of the system, respectively.

[0022] The electrical model of the motor is represented as follows: (5) In the formula, , , These are the d-axis and q-axis inductances of the motor, respectively. , These are the d-axis and q-axis voltages of the motor, respectively. , These are the d-axis and q-axis currents of the motor, respectively. This refers to the stator resistance of the motor. For permanent magnet flux linkage; ω is the rotor's electric angular velocity.

[0023] The mechanical motion equations considering nonlinear factors are as follows: (6) In the formula, This represents a gap nonlinear model; Combining equations (1) to (6), the electromechanical coupling model of the PMSM actuator is obtained as follows: (7) In the formula, the equivalent rotational inertia of the motor and the input terminal is... ; It is the extreme logarithm; This is the equivalent rotational inertia of the motor. is the coefficient of viscous friction.

[0024] 2. Design of LESO (Linear Extended State Observer) A linear extended state observer is designed based on a hyperlocal model to observe the total disturbance of the system in real time.

[0025] The mechanical motion equation in equation (6) can be rewritten in hyperlocal model form: (8) Equation (8) is taken as the hyperlocal model of the speed loop. Then the LESO representation of the speed loop is: (9) in, ; These are the observed values ​​for rotational speed and disturbance, respectively. This represents the actual disturbance value. , The equivalent rotational inertia of the system, This refers to the motor speed. and This is the observer gain.

[0026] The electrical model of the motor in equation (5) is rewritten in the form of a hyperlocal model: (10) In the formula, the total disturbance , , .

[0027] Taking equation (10) as a hyperlocal model of the current loop, the LESO expression of the current loop is: (11) In the formula, , Let the observed values ​​be for the d-axis and q-axis currents, respectively. , These are the observed values ​​for the d-axis and q-axis perturbations, respectively. ~ This is the observer gain. , These are the bandwidths of the speed loop and current loop observers, respectively.

[0028] 3. Disturbance decomposition and contribution calculation Based on the established electromechanical coupling discrete model, the total disturbance is decomposed into multiple components such as motor friction, transmission clearance, transmission friction and unmodeled dynamics, and the contribution of each component is calculated.

[0029] Total system disturbance Decomposed into: (12) In the formula, the total system disturbance is... Decomposed into motor friction terms gap item Friction items in the transmission system Modeling error terms .

[0030] Calculate the contribution of each component: (13) In the formula, It is a very small constant to prevent division by zero.

[0031] Design adaptive weighting factors: (14) In the formula, The rotational speed error threshold is 30 revolutions per minute. To simplify real-time calculations, a principal component selection strategy is adopted, applying full weight compensation only to the component with the largest contribution. , This is an adaptive weighting factor.

[0032] 4. Design a backstepping adaptive controller A backstepping adaptive controller is designed based on contribution, and feedforward compensation is performed in conjunction with a linearly extended state observer.

[0033] use The control strategy and design steps are as follows: a) Define Lyapunov functions ,in, .

[0034] b) Define the speed tracking error Design virtual control variables: (15) In the formula, To control the gain.

[0035] Through virtual control quantity Make Substituting the error dynamics equation, which includes a disturbance compensation term, into the equation yields the virtual control law.

[0036] c) Define current tracking error , Design voltage control law: (16) In the formula, , To control the gain, , For perturbation observations, , For the backstep control voltage, The second derivative of the rotational speed observation. The derivative of the perturbation decomposition term, The disturbance weights are defined; the voltage control law includes the disturbance observations. , Feedforward compensation and error feedback terms.

[0037] By tracking the current error, the extended Lyapunov function incorporating the current error is made possible. Differential negative fixed. The voltage control law includes the observation of the disturbance. , Feedforward compensation and error feedback terms.

[0038] 5. Design of a deadbeat prediction controller To compensate for the one-step delay in the digital control system, the second-order Euler method is used for current prediction. The voltage prediction equation considering the one-step delay is as follows: (17) The designed deadbeat predictive controller can force the current tracking error to approach zero within one control cycle, and has an extremely fast dynamic response.

[0039] 6. Dynamic weight optimization and fusion A dynamic weight optimization strategy is adopted to adjust the fusion weights in order to balance the contributions of backstep control and predictive control.

[0040] The weight adjustment is shown in the following formula: (18) In the formula, To integrate weights, The minimum weight value, The maximum weight value, The magnitude of the current error. The current error amplitude threshold, The amplitude of the dominant disturbance component. The threshold value for the dominant disturbance component. As the weight of the contribution of the dominant disturbance component, This is an adaptive weighting factor.

[0041] The calculation method for the control voltage of the SPMSM actuator based on backstepping control and predictive control is as follows: Initialize the weight parameters. , , , , These thresholds can be determined through simulation under rated operating conditions.

[0042] Then, in each control cycle: a) Calculate the current error amplitude .

[0043] b) Obtain the amplitude of the current dominant disturbance component and its contribution weight .

[0044] c) Calculate the real-time fusion weights according to equation (18) .

[0045] d) Calculate the final control voltage: , The dq-axis control voltage is for the backstepping control method. The dq-axis control voltage is obtained using the deadbeat current predictive control (DPCC) method.

[0046] The final control voltage is then transformed by Park inverse transformation and space vector pulse width modulation to drive the three-phase inverter and control the operation of the permanent magnet synchronous motor.

[0047] Stability analysis was performed on the multi-source nonlinear perturbation suppression method provided in this embodiment, and the Lyapunov function of the entire cascaded system was constructed. ,in The Lyapunov function is the function of the backstepping adaptive control part. This is the current error vector.

[0048] Because deadbeat current predictive control can eliminate the current error of the next step within one step, i.e. ,therefore , The backstep adaptive part has been proven to satisfy... (When the error is not zero). Therefore, the Lyapunov function difference of the cascaded system It satisfies the asymptotic stability condition.

[0049] The method proposed in this embodiment is verified on a dual-motor counter-traction experimental platform. The motor parameters are as follows: , =4.2 Ω, =5.25 mH, =0.17 Wb, =3.45e-3 / Kg m 2 Controller parameters: They are 1000 / 12000 / 8000 respectively. They are 850 and 8500 rad / s respectively.

[0050] Comparing the LESO-BS-DPCC method proposed in this embodiment with the single LESO-BS and LESO-DPCC methods: Ⅰ. For example Figure 5 , Figure 6 As shown, constant load start-up and speed regulation: the given speed jumps from 50 rpm to 600 rpm, and then the rated load is suddenly applied. The LESO-BS-DPCC method has no overshoot during start-up, the shortest stabilization time (0.17 s), the smallest speed drop after loading (8 rpm), and the fastest recovery (0.20 s).

[0051] II. For example Figures 7 to 9 As shown, variable load operation: at a steady speed of 600 rpm, a sinusoidal or random load disturbance with an amplitude of 23 Nm is applied. The speed fluctuation amplitude (62 rpm) and maximum current (11.2 A) of the LESO-BS-DPCC method are both smaller than those of the comparative method.

[0052] III. For example Figure 10 As shown, the total harmonic distortion (THD) of the A-phase current is the lowest under various operating conditions using the LESO-BS-DPCC method, ranging from approximately 4.57% to 10.32%, indicating high current control accuracy and good waveform quality.

[0053] Experimental results show that the method proposed in this embodiment can effectively suppress multi-source nonlinear disturbances and outperforms single control strategies in terms of dynamic response, steady-state accuracy and robustness.

[0054] Example 2 This embodiment provides a multi-source nonlinear disturbance suppression system for permanent magnet synchronous motors, used to implement the method described in Embodiment 1.

[0055] This system can be integrated into a motor driver or a host computer, including: Signal acquisition and processing unit: includes current sensor, speed / position encoder interface, and Clark and Park conversion modules.

[0056] Disturbance Observation and Calculation Unit: Runs the LESO algorithm and the nonlinear disturbance decomposition and contribution calculation program.

[0057] Backstepping adaptive control unit: Calculates based on reference speed, observed state, and disturbance information. .

[0058] Deadbeat predictive control unit: Calculates based on reference current (from the outer loop or given) and motor status. .

[0059] Dynamic optimization and fusion unit: Real-time calculation of fusion weights And perform weighted fusion of control voltages.

[0060] Modulation and drive unit: Performs Park inverse transform and SVPWM algorithm to generate PWM signal to drive three-phase inverter.

[0061] Example 3 This embodiment provides a computer-readable storage medium, such as ROM, RAM, flash memory, hard disk, etc., on which a computer program is stored. When the program is executed by a processor (such as DSP, FPGA, ARM, etc.), it can implement all the steps of the multi-source nonlinear disturbance suppression method for permanent magnet synchronous motors described in Embodiment 1.

[0062] Example 4 This embodiment provides an electronic device, such as a dedicated controller for permanent magnet synchronous motors, an industrial motion control card, or a general-purpose industrial computer. The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the stored computer program, it implements the steps of the method described in Embodiment 1, thereby achieving high-precision control of the connected permanent magnet synchronous motor.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for suppressing multi-source nonlinear disturbances in a permanent magnet synchronous motor, characterized in that, The method includes: An electromechanical coupling discrete model of the permanent magnet synchronous motor actuator is established, including the equivalent dynamic model of the transmission system, the nonlinear disturbance model, and the electrical model of the motor. A linear extended state observer is designed based on a hyperlocal model to observe the total disturbance of the system in real time. Based on the established electromechanical coupling discrete model, the total disturbance of the system is decomposed into multiple nonlinear disturbance factor components, and the contribution of each component is calculated. A backstep adaptive controller is designed based on contribution, and feedforward compensation is performed in conjunction with a linearly extended state observer. Design a deadbeat predictive current controller and generate a control voltage by weighted fusion of a dynamic weight optimization strategy and a backstep adaptive controller. The final generated d-axis and q-axis voltage control values ​​are applied to the permanent magnet synchronous motor actuator.

2. The method according to claim 1, characterized in that, The equivalent dynamic model of the transmission system is expressed as: In the formula, The equivalent rotational inertia of the system, Input shaft speed The derivative, For electromagnetic torque, For load torque, The equivalent transmission ratio includes fluctuations; The moment of inertia of the equivalent component at the input end. The moment of inertia of the equivalent component at the output end. For a constant transmission ratio, For transmission ratio fluctuation; Nonlinear perturbation models include gap errors Transmission error and LuGre friction model The gap error is approximated using a continuously differentiable function: In the formula, For the angle of the sun gear, For time, For the input angle error, The gap width, The slope constant is... This is the gap damping coefficient. To account for the overall transmission speed error; The LuGre friction model is expressed as: In the formula, This is the stiffness coefficient. This is the pre-sliding damping coefficient. It is the coefficient of viscous friction; The average deformation of the bristles is unmeasurable. , and These are Coulomb friction, static friction, and the Stribeck velocity of the system, respectively. The electrical model of the motor is represented as follows: In the formula, , , Motors Shaft inductance; For motor Shaft voltage; For motor shaft current; This refers to the stator resistance of the motor. For permanent magnet flux linkage; The rotor's electric angular velocity; The mechanical motion equations considering nonlinear factors are as follows: In the formula, This represents a gap nonlinear model; Combining the above models and equations, the electromechanical coupling model of the PMSM actuator is obtained as follows: In the formula, the equivalent rotational inertia of the motor and the input terminal is... ; It is the extreme logarithm; This is the equivalent rotational inertia of the motor. is the coefficient of viscous friction.

3. The method according to claim 2, characterized in that, The design of a linearly extended state observer based on a hyperlocal model includes: The mechanical motion equations are rewritten in hyperlocal model form and used as a hyperlocal model of the speed loop: The state observer for linear expansion of the rotational speed loop is designed based on the rotational speed loop hyperlocal model as follows: in, ; These are the observed values ​​for rotational speed and disturbance, respectively. The electrical model of the motor is rewritten as a hyperlocal model and used as a hyperlocal model of the current loop: In the formula, the total disturbance , , ; The current loop linear expansion state observer is designed based on the current loop hyperlocal model as follows: ； In the formula, , Let the observed values ​​be for the d-axis and q-axis currents, respectively. , These are the observed values ​​for the d-axis and q-axis perturbations, respectively. ~ For observer gain; , These are the bandwidths of the speed loop and current loop observers, respectively.

4. The method according to claim 3, characterized in that, Based on the established electromechanical coupling discrete model, the total system disturbance is divided into multiple nonlinear disturbance factors, namely motor friction, transmission backlash, transmission friction, and unmodeled dynamic components, which are expressed as follows: In the formula, the total system disturbance is... Decomposed into motor friction terms gap item Friction items in the transmission system Modeling error term ; Calculate the contribution of each component: In the formula, A tiny constant set to prevent division by zero.

5. The method according to claim 4, characterized in that, Designing a backstepping adaptive controller based on contribution includes designing virtual control variables: Design voltage control law: In the formula, , To control the gain, , For perturbation observations, , For d-axis and q-axis backstepping control voltage, The second derivative of the rotational speed observation. The derivative of the perturbation decomposition term, The disturbance weights are defined; the voltage control law includes the disturbance observations. , Feedforward compensation and error feedback terms.

6. The method according to claim 5, characterized in that, Design a deadbeat predictive current controller, considering the voltage prediction equation with a one-beat delay as follows: The fusion weights are adjusted using a dynamic weight optimization strategy, and the deadbeat predictive current controller and the backstep adaptive controller are weighted and fused. The fusion weights are expressed as follows: In the formula, To integrate weights, The minimum weight value, The maximum weight value, The magnitude of the current error. The current error amplitude threshold, The amplitude of the dominant disturbance component. The threshold value for the dominant disturbance component. As the weight of the contribution of the dominant disturbance component, These are the perturbation weighting coefficients; As an adaptive weighting factor, This is the speed error threshold; The final control voltage is obtained by weighted fusion of the deadbeat predictive current controller and the backstep adaptive controller. In the formula, The dq-axis control voltage is for the backstepping control method. The dq axis control voltage is for deadbeat current prediction control.

7. The method according to claim 6, characterized in that, Dynamic weight optimization strategies include first initializing the weight parameters, i.e., initializing... , , , and Then, in each control cycle, perform the following operations to update the fusion weights: Calculate the current error amplitude ; Obtain the amplitude of the current dominant disturbance component and its contribution weight ; Calculate real-time fusion weights .

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method as described in any one of claims 1 to 7.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the stored computer program, it implements the method as described in any one of claims 1 to 7.

Images