A self-adaptive fuzzy dynamic self-triggered discrete control method for permanent magnet synchronous motor

Abstract

The application belongs to the technical field of permanent magnet synchronous motor tracking control, and specifically discloses a kind of permanent magnet synchronous motor adaptive fuzzy dynamic self-triggering discrete control method.The method establishes a kind of dynamic self-triggering mechanism, according to the dynamic performance of system adjusts corresponding self-triggering parameter, to update the control law and adaptive law of permanent magnet synchronous motor, break the restriction that traditional event trigger method needs to monitor trigger condition continuously, effectively reduce the calculation burden.Secondly, the application processes the nonlinear term in the system through the adaptive fuzzy control method, and solves the causality contradiction and complexity explosion problem in the traditional discrete backstepping method by using the instruction filtering method, and the designed compensation signal reduces the influence of filtering error, enhances the control accuracy of the system.Simulation results and experimental results show that the application can save more computing resources while quickly tracking the reference signal, and has good tracking effect and robustness.

Description

Technical Field

[0001] This invention belongs to the field of permanent magnet synchronous motor tracking control technology, specifically relating to an adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs) are widely used in engineering fields due to their simple structure, high efficiency, and long service life. However, PMSMs exhibit strong coupling, nonlinearity, and multivariable characteristics, and are easily affected by load disturbances and parameter changes. Therefore, how to accurately control PMSMs to maintain their good dynamic performance has become a research problem for many scholars. Currently, many experts and scholars have proposed many control methods, such as backstepping control, sliding mode control, adaptive control, and robust control. Designing a controller for PMSMs using the traditional backstepping method can lead to a "complexity explosion" problem due to repeated differential calculations. Furthermore, since PMSMs are complex high-order nonlinear systems, their stator resistance, load torque, etc., undergo difficult-to-measure changes during operation. External disturbances and model uncertainties can affect system performance, making it difficult for the designed controller to achieve precise control.

[0003] With the continuous development of modern technology, high-performance computers have been widely used in the field of control. Compared with continuous-time control, discrete-time control has advantages in stability and feasibility, and is more suitable for computer control. Some feasible studies have been conducted on the application of discrete-time control in PMSMs. For example, in some engineering scenarios, many distributed PMSM drive systems rely on time-triggered control (TTC) to achieve network communication. This control method is not conducive to reducing the computational burden of the system. To reduce the waste of computing resources, many experts and scholars have proposed various control methods, such as event-triggered control and self-triggered control. Compared with event-triggered control, self-triggered control can avoid continuous monitoring of triggering conditions, further reducing the computational burden. However, there is currently no research on self-triggered control for discrete PMSM systems. Summary of the Invention

[0004] The purpose of this invention is to propose an adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors, so as to enable permanent magnet synchronous motor systems to quickly track preset desired signals while saving computing resources.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: An adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors includes the following steps: Step 1. Establish a discrete model of the permanent magnet synchronous motor; Step 2. Based on the discrete model of the permanent magnet synchronous motor established in Step 1, an adaptive fuzzy dynamic self-triggering discrete controller for the permanent magnet synchronous motor is designed according to the principle of adaptive fuzzy control method and instruction filtering backstepping method; the system state at the current triggering moment is used to predict and calculate the next triggering moment, so as to avoid judging the triggering condition at every sampling moment. Step 3. Using the adaptive fuzzy dynamic self-triggering discrete controller for the permanent magnet synchronous motor designed in Step 2, the permanent magnet synchronous motor can quickly track the desired signal while reducing the computational burden.

[0006] Furthermore, based on the aforementioned adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors, this invention also proposes a corresponding adaptive fuzzy dynamic self-triggering discrete control system for permanent magnet synchronous motors, which adopts the following technical solution: An adaptive fuzzy dynamic self-triggering discrete control system for permanent magnet synchronous motors includes the following modules: The model building module is used to build discrete models of permanent magnet synchronous motors. The controller design module is used to design an adaptive fuzzy dynamic self-triggering discrete controller for a permanent magnet synchronous motor based on the established discrete model of the permanent magnet synchronous motor, according to the adaptive fuzzy control method and the instruction filtering backstepping method. It uses the system state at the current triggering moment to predict and calculate the next triggering moment, so as to avoid judging the triggering condition at every sampling moment. And a tracking control module, which utilizes the designed adaptive fuzzy dynamic self-triggering discrete controller for permanent magnet synchronous motors to enable permanent magnet synchronous motors to quickly track the desired signal while reducing the computational burden.

[0007] Furthermore, based on the aforementioned adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors, this invention also proposes a computer device comprising a memory and one or more processors. Executable code is stored in the memory. When the processor executes the executable code, it implements the steps of the aforementioned adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors.

[0008] Furthermore, based on the aforementioned adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors, this invention also proposes a computer-readable storage medium storing a program that, when executed by a processor, implements the steps of the aforementioned adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors.

[0009] The present invention has the following advantages: As described above, this invention proposes an adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors (PMSMs). Based on a discrete-time model, this method achieves dynamic self-triggering control of the PMSM discrete system. This invention solves the problems of "causal contradiction" and "complexity explosion" in traditional backstepping methods through instruction filtering and introduces compensation signals to reduce the impact of filtering errors. Simultaneously, it utilizes an adaptive fuzzy control method to handle the unknown nonlinear terms of the PMSM discrete system, reducing their impact on system performance. Furthermore, the dynamic self-triggering mechanism designed in this invention only needs to calculate the next triggering time based on the control signal at the current triggering time and the system error. This breaks the limitation of traditional event-triggered methods requiring strict and continuous monitoring of triggering conditions, avoiding frequent calculations of control signals in the PMSM intelligent control system. This effectively reduces the computational pressure caused by frequent control signal calculations, saves computational resources for the PMSM system, and is more conducive to physical implementation in engineering practice. This invention effectively enables the PMSM to quickly track a preset reference signal while saving computational resources, and the designed adaptive fuzzy dynamic self-triggering discrete controller for the PMSM exhibits excellent dynamic performance. Attached Figure Description

[0010] Figure 1 This is a flowchart of the adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors in an embodiment of the present invention; Figure 2 This is a system block diagram of the adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors in an embodiment of the present invention; Figure 3 The figure shows a simulation of the speed response curve of a permanent magnet synchronous motor using the control method of this invention. Figure 4 The simulation diagram shows the speed response curve of a permanent magnet synchronous motor using a comparative method. Figure 5 The figure shows a simulation of the speed tracking error curve of a permanent magnet synchronous motor using the control method of this invention. Figure 6 The simulation diagram shows the speed tracking error curve of a permanent magnet synchronous motor using a comparison method. Figure 7 A permanent magnet synchronous motor using the control method of this invention Simulation diagram of shaft voltage; Figure 8 For permanent magnet synchronous motors using a comparative method Simulation diagram of shaft voltage; Figure 9 A permanent magnet synchronous motor using the control method of this invention Simulation diagram of shaft voltage; Figure 10 For permanent magnet synchronous motors using a comparative method Simulation diagram of shaft voltage; Figure 11 A permanent magnet synchronous motor using the control method of this invention Simulation diagram of shaft current; Figure 12 For permanent magnet synchronous motors using a comparative method Simulation diagram of shaft current; Figure 13 A permanent magnet synchronous motor using the control method of this invention Axis current experimental diagram; Figure 14 For permanent magnet synchronous motors using a comparative method Simulation diagram of shaft current; Figure 15 The speed response curve of a permanent magnet synchronous motor using the control method of this invention is shown. Figure 16 The speed response curves of permanent magnet synchronous motors using a comparative method are shown. Figure 17 A permanent magnet synchronous motor using the control method of this invention Shaft voltage trigger interval diagram; Figure 18 For permanent magnet synchronous motors using a comparative method Shaft voltage trigger interval diagram; Figure 19 A permanent magnet synchronous motor using the control method of this invention Shaft voltage trigger interval diagram; Figure 20 For permanent magnet synchronous motors using a comparative method Shaft voltage trigger interval diagram. Detailed Implementation

[0011] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Example 1 This embodiment 1 proposes an adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors (PMSMs). Addressing the practical requirements of saving computational resources and the inherent nonlinearity issues of PMSMs, this method designs an adaptive fuzzy dynamic self-triggering discrete controller for the PMSM system. This allows the tracking error to converge within a bounded range while saving computational resources. Instruction filtering technology is used to solve the inherent "causal contradiction" and "complexity explosion" problems in traditional backstepping methods. Simultaneously, an adaptive fuzzy control method is combined to handle the unknown nonlinear terms of the PMSM system, reducing their impact on system performance.

[0012] like Figure 1 As shown in the figure, this embodiment describes an adaptive fuzzy dynamic self-triggering discrete control method for a permanent magnet synchronous motor, including the following steps: Step 1. Establish a discrete model of the permanent magnet synchronous motor.

[0013] Establishing a permanent magnet synchronous motor The coordinate axis model is shown in formula (1): (1) in, Indicates the rotor angular velocity. This represents the moment of inertia of a permanent magnet synchronous motor. This indicates the number of pole pairs in a permanent magnet synchronous motor. and for Stator inductance in coordinate system , They are respectively , Excitation current on the shaft, This refers to the magnetic flux linkage generated by the permanent magnets in a permanent magnet synchronous motor. This represents the friction coefficient of a permanent magnet synchronous motor. This represents the load torque of the permanent magnet synchronous motor. This represents the stator resistance of a permanent magnet synchronous motor. , They represent , shaft voltage, and It is the input signal of the permanent magnet synchronous motor system.

[0014] The permanent magnet synchronous motor model is described using the Euler method as a discrete model as shown in equation (2): (2) in, Indicates the sampling time. and They represent Time and The rotor angular velocity at time t, and They represent Time and time Excitation current on the shaft, and They represent Time and time Excitation current on the shaft, , They represent time , Voltage on the shaft.

[0015] Define variables , , , , , , , , , , , , , as follows: , , ; , , , ; , , , ; , , ; The simplified permanent magnet synchronous motor The coordinate axis discretization model is shown in equation (3): (3).

[0016] Step 2. Based on the discrete model of the permanent magnet synchronous motor established in Step 1, an adaptive fuzzy dynamic self-triggering discrete controller for the permanent magnet synchronous motor is designed according to the adaptive fuzzy control method and the principle of instruction filtering backstepping method.

[0017] By designing a dynamic self-triggering mechanism, the system state at the current triggering moment of the permanent magnet synchronous motor is used to predict and calculate the next triggering moment. This eliminates the need for the control signal to be calculated at every sampling moment. Instead, the next triggering moment can be calculated at the current self-triggering moment using the current control signal and system error. This avoids the need for traditional event-triggered mechanisms to calculate and judge the control signal and triggering threshold at every sampling moment, thereby reducing the computational burden.

[0018] It is important to note that designing an adaptive fuzzy dynamic self-triggering discrete controller for a PMSM using the traditional backstepping method can lead to a "complexity explosion" problem due to repeated difference calculations, making controller design difficult. This invention employs a command filtering method to address the "causal contradiction" and "complexity explosion" problems. Furthermore, since a PMSM is a complex high-order nonlinear system, its stator resistance, load torque, and other parameters undergo difficult-to-measure changes during operation. External disturbances and model uncertainties can affect system performance. This invention utilizes an adaptive fuzzy control method to handle unknown nonlinear terms in the system, thereby reducing their impact on system performance.

[0019] Step 2.1. Establish a dynamic self-triggering mechanism.

[0020] In order to update the control law and adaptive law of the permanent magnet synchronous motor, and at the same time avoid the permanent magnet synchronous motor system from continuously monitoring the triggering conditions and reducing the waste of computing resources, this invention adopts a dynamic self-triggered control (DSTC) strategy.

[0021] assumed , and These are the three most closely spaced self-triggered moments.

[0022] The dynamic self-triggering mechanism is established as follows: (4) in, Indicates the first The output of the controller at each self-triggering moment It is the actuator input, i.e., the control law. Self-triggering time It is a subset of traditional sampling sequences. Indicates the first One self-triggered moment; , , It is a positive self-triggering design parameter. This indicates taking the absolute value. This indicates taking the maximum value.

[0023] ;in , and These are the designed self-triggering parameters, and , It is a systematic error. It is the hyperbolic tangent function.

[0024] ,in It is the interval between the current self-triggered moment and the previous self-triggered moment. and They represent the first The self-triggered moment and the first The input of the self-triggering executor.

[0025] when At that time, the dynamic self-triggering mechanism is triggered, updating the control law. At the same time, the next self-triggering time is calculated, i.e. ;when At that time, the dynamic self-triggering mechanism was not triggered, and the control law... Remain in the state of the previous self-triggered moment.

[0026] Based on the above dynamic self-triggering mechanism, the following exists: ,in It is the controller output.

[0027] Step 2.2. Construct the system error and error compensation signals.

[0028] Define the instruction filter as follows: ; in and These are the positive design parameters of the instruction filter. Represents a virtual control law; , This indicates that the input of the filter is The output of the second-order instruction filter at the current step. , This indicates that the corresponding value will be output in the next step. = , It is an input virtual control law The output signal of the time command filter.

[0029] Define systematic error , , as follows: (5) in, The preset tracking speed signal.

[0030] Define error compensation signal , and as follows: (6) in , , This represents the final error after the system error has been compensated by the compensation signal.

[0031] Step 2.3. Design an adaptive fuzzy dynamic self-triggering discrete controller for the permanent magnet synchronous motor.

[0032] According to equations (3), (5), and (6), we get: (7) in This indicates the value of the next step number after the system error has been compensated by the compensation signal. To preset the tracking speed signal, This is the error compensation signal.

[0033] Selected virtual control law and error compensation signal for: (8) (9) Substituting equations (8) and (9) into equation (7), we get: (10) Choosing Lyapunov functions for: ; According to equation (10), we get First-order difference for: (11) in , It is a normal number, meaning that the load torque of a permanent magnet synchronous motor system is bounded.

[0034] According to Young's inequality, we have: (12) Pick According to equations (2), (5), and (6), we get: (13) in ; , Indicates the first The output of the controller at each self-triggering moment is Voltage on the shaft; The input virtual control law is The output signal of the time command oscillator express The value of the signal after compensation is the next step value.

[0035] Fuzzy logic systems exist , making .

[0036] in, For the ideal weight column vector, ; To approximate the error, and satisfy... , For any arbitrarily small constant; This represents a fuzzy basis function vector.

[0037] The weight estimation error is , yes The estimated value.

[0038] make ;in These are positive design parameters.

[0039] Design a self-triggering control law With Adaptive Law for: (14) (15) in, These are positive design parameters. for The estimated value.

[0040] According to equations (13) and (14), we get: (16) in Indicates the first The estimated value of the ideal weight column vector at each self-triggered moment.

[0041] Pick According to equations (2), (5), and (6), we get: (17) in ; , Indicates the first The output of the controller at each self-triggering moment is Voltage of the shaft Indicates systematic error. express The value of the signal after compensation is the next step value.

[0042] Fuzzy logic systems exist , so that: ; in, For the ideal weight column vector, ; To approximate the error, and satisfy... , For any arbitrarily small constant; This represents a fuzzy basis function vector.

[0043] Weight estimation error , yes The estimated value.

[0044] Design a self-triggering control law With Adaptive Law for: (18) (19) in, These are positive design parameters. for The estimated value.

[0045] According to equations (17) and (18), we get: (20) in Indicates the first The estimated value of the ideal weight column vector at each self-triggered moment.

[0046] Step 2.5. After completing the design of the adaptive fuzzy dynamic self-triggering discrete controller for the permanent magnet synchronous motor, perform boundedness analysis of the weighted error value and system stability analysis on the permanent magnet synchronous motor system controlled by the adaptive fuzzy dynamic self-triggering discrete controller.

[0047] Step 2.5.1. Boundedness analysis of weighted error values.

[0048] The boundedness of the weight error value is proven by considering two cases: one at the self-triggering time. and self-trigger interval time , Indicates the number of trigger intervals. Each sampling time.

[0049] Based on weighted error value Combining equations (15) and (19), we get: (twenty one) I. At the self-triggered moment Adaptive law renew.

[0050] Constructing Lyapunov functions for: (twenty two) According to equations (21) and (22), we obtain The first difference is divided into: (twenty three) in, , , , , This indicates a search for the norm.

[0051] By Young's inequality: (twenty four) according to Substituting equation (24) into equation (23) yields: (25) make Then equation (25) simplifies to: (26) make ,have to: ; ; in , Therefore, the weight error value is bounded at the self-trigger time.

[0052] II. During the self-trigger interval The adaptive law remains unchanged, and the control signal remains unchanged until the next self-triggering time. The weight error value is constant within the self-trigger interval; it only needs to be proven that... It is bounded. This represents the weight estimation error at the first sampling time within the trigger interval.

[0053] According to equation (26): (27) Equation (27) is further transformed into: (28) ; in, Therefore, the weight error value at the self-trigger interval... It is also bounded.

[0054] Step 2.5.2. System stability analysis.

[0055] III. At the self-triggered moment The system's adaptive law and self-triggering control law are updated.

[0056] Choosing Lyapunov functions for: (29) in, It is a constant, and , , It is a constant, and .

[0057] According to equations (11), (16), (20), and (26), we get: (30) in, , It is a constant. , It is a constant.

[0058] Select appropriate parameters , , , , as well as , so that: , , , ,Right now ,in , It is a constant; therefore Bounded, and further Bounded; because Bounded, obtained Bounded; thus, it is concluded that Bounded. Therefore, all signals in a closed-loop system are bounded.

[0059] IV. During the self-trigger interval The system's adaptive law has not been updated. .

[0060] Then the Lyapunov function for: ,get: (31)

[0061] in , This represents the weight estimate at the self-triggered moment.

[0062] according to Equations (14), (18), and (28) are transformed into equation (31) using Young's inequality: (32) in: ; , , These are the dynamic self-triggering parameters of the design, where , This represents the weight estimation error value at the self-triggered moment.

[0063] Known weight error value It is bounded at any given moment, therefore It is bounded, that is... ,in , It is a constant; choose appropriate parameters. , , , , , , , ,and ,make , ,but It is bounded; similarly, at the self-trigger interval, all signals in the closed-loop system are bounded.

[0064] Step 3. Using the adaptive fuzzy dynamic self-triggering discrete controller for the permanent magnet synchronous motor designed in Step 2, the permanent magnet synchronous motor can quickly track the desired signal while reducing the computational burden.

[0065] Figure 2 The diagram illustrates a composite permanent magnet synchronous motor consisting of an adaptive fuzzy dynamic self-triggering discrete controller, coordinate transformation, space vector pulse width modulation (SVPWM), and an inverter, as described in this invention.

[0066] in This represents the desired rotor angular velocity. This represents a two-phase stationary coordinate system. , The axes representing a two-phase stationary coordinate system. Representing a two-phase stationary coordinate system Voltage of the shaft Representing a two-phase stationary coordinate system Voltage of the shaft This represents a three-phase coordinate system.

[0067] like Figure 2 As shown, the complete closed-loop processing flow of the system is as follows: The system first calculates the desired rotor angular velocity... The actual angular velocity acquired by the encoder The signal is fed into the adaptive fuzzy dynamic self-triggering discrete controller of the permanent magnet synchronous motor, and the output is... Shaft ideal voltage command , The command is updated via a dynamic self-triggering mechanism to obtain the final control voltage. , Combined with electrical angle Transformed by the inverse Park transform Two-phase stationary coordinate system voltage , Subsequently , The SVPWM module generates PWM pulses to drive the inverter to output three-phase AC voltage to the permanent magnet synchronous motor, enabling it to run. During motor operation, the encoder collects the rotor position in real time. With angular velocity Simultaneously collect three-phase current , , The three-phase current is obtained by Clark transformation. shaft current , , and then combine Converted to Park transform shaft current , ,final , and Together, they are fed back to the controller, forming a complete speed-current dual closed-loop control process.

[0068] This invention's controller overcomes the "causal contradiction" and "complexity explosion" problems of traditional backstepping methods through instruction filtering, and utilizes an adaptive fuzzy control method to handle the unknown nonlinear terms of the system. The controller output... and The actual output is obtained after conversion by the inverter. , , This allows the permanent magnet synchronous motor system to quickly track preset desired values ​​while saving computing resources.

[0069] The adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors proposed in this invention is simulated in a virtual environment to verify the feasibility of the proposed control method.

[0070] The parameters for the permanent magnet synchronous motor are selected as follows: , , , , , , .

[0071] Select reference signal Sampling time The initial conditions are all 0, and the load torque is: .

[0072] Select the following controller parameters: , , , , , , , , , , , , , , , , , .

[0073] The control method of this invention is compared with the static self-triggered control (SSTC) method using fixed self-triggered parameters through simulation. In the SSTC method, Fixed and unchanging, The remaining control parameters and PMSM parameters are the same as those in the control method of this invention. Simulation results are as follows: Figures 3 to 14 As shown.

[0074] Figure 3 , Figure 5 , Figure 7 , Figure 9 , Figure 11 and Figure 13 The simulation results are for a permanent magnet synchronous motor controlled using the DSTC method of this invention. Figure 4 , Figure 6 , Figure 8 , Figure 10 , Figure 12 and Figure 14The simulation results for a permanent magnet synchronous motor using the comparative method, namely the SSTC method, are presented. From... Figures 3 to 6 It can be seen that both methods can effectively track the speed curve of a permanent magnet synchronous motor, but the method of this invention can better reduce the computational burden of the system. Figure 7 and Figure 8 The permanent magnet synchronous motors representing the control method and comparison method of the present invention are respectively shown. Axis voltage curve, Figure 9 and Figure 10 The permanent magnet synchronous motors representing the control method and comparison method of the present invention are respectively shown. Voltage simulation diagram of the shaft. Figure 11 and Figure 12 The permanent magnet synchronous motors representing the control method and comparison method of the present invention are respectively shown. Simulation diagram of shaft current. Figure 13 and Figure 14 The permanent magnet synchronous motors representing the control method and comparison method of the present invention are respectively shown. The current simulation diagram of the shaft shows that the voltage and current of the permanent magnet synchronous motor using the control method of this invention fluctuate within a very small range and can be limited to a reasonable range.

[0075] Experiments were conducted on the LINKS-PMSM AC servo experimental platform to further verify and analyze the effectiveness of the adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors designed in this invention.

[0076] The platform consists of a permanent magnet synchronous motor, a simulation host, and a servo driver. The permanent magnet synchronous motor has a rated power of 400W and a rated torque of 1.27N·m.

[0077] The control algorithm was written in MATLAB / Simulink on the simulation host, and the Link-RT software provided a real-time operating environment for the Simulink model.

[0078] The experiment's preset desired speed was 200 r / min. At 10 s, the desired speed abruptly increased to 300 r / min; at 20 s, the desired speed increased to 400 r / min; and at 30 s, the desired speed decreased back to 300 r / min. The load torque was... The sampling time was 0.0001s.

[0079] Select the controller parameters for the experiment: , , , , , , , , , , , , , , , , , The experimental results are as follows: Figures 15 to 20 As shown.

[0080] Figure 15 The speed response curve of a permanent magnet synchronous motor using the control method of this invention is shown. Figure 16 The speed response curves of permanent magnet synchronous motors using a comparative method are shown. Figure 17 A permanent magnet synchronous motor using the control method of this invention Shaft voltage trigger interval diagram, Figure 18 For permanent magnet synchronous motors using a comparative method Shaft voltage trigger interval diagram, Figure 19 A permanent magnet synchronous motor using the control method of this invention Shaft voltage trigger interval diagram, Figure 20 For permanent magnet synchronous motors using a comparative method Shaft voltage trigger interval diagram. According to... Figures 15 to 20 It can be seen that the speed response of the permanent magnet synchronous motor using both methods can track the desired speed, but the control method of this invention can better reduce the number of triggers and effectively reduce the computational burden of the system.

[0081] The simulation and experimental results above show that the adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors proposed in this invention can quickly track the reference signal while saving more computational resources, and has good tracking effect and robustness. Therefore, the control method of this invention has practical significance.

[0082] Example 2 This embodiment 2 describes an adaptive fuzzy dynamic self-triggering discrete control system for a permanent magnet synchronous motor. This adaptive fuzzy dynamic self-triggering discrete control system for a permanent magnet synchronous motor is based on the same inventive concept as the adaptive fuzzy dynamic self-triggering discrete control method for a permanent magnet synchronous motor in embodiment 1 above.

[0083] An adaptive fuzzy dynamic self-triggering discrete control system for a permanent magnet synchronous motor includes the following modules: The model building module is used to build discrete models of permanent magnet synchronous motors. The controller design module is used to design an adaptive fuzzy dynamic self-triggering discrete controller for a permanent magnet synchronous motor based on the established discrete model of the permanent magnet synchronous motor, according to the adaptive fuzzy control method and the instruction filtering backstepping method. It uses the system state of the permanent magnet synchronous motor at the current triggering moment to predict and calculate the next triggering moment, so as to avoid judging the triggering condition at every sampling moment. And a tracking control module, which utilizes the designed adaptive fuzzy dynamic self-triggering discrete controller for permanent magnet synchronous motors to enable permanent magnet synchronous motors to quickly track the desired signal while reducing the computational burden.

[0084] It should be noted that any content not mentioned in the above-described functional modules of the system described in Embodiment 2 can be referred to the step description of the corresponding method in Embodiment 1 above, and will not be repeated in detail here.

[0085] Example 3 This embodiment 3 describes a computer device including a memory and one or more processors. Executable code is stored in the memory. When the processor executes the executable code, it implements the steps of the adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors described in embodiment 1 above.

[0086] Example 4 This embodiment 4 describes a computer-readable storage medium storing a program that, when executed by a processor, is used to implement the steps of the adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors in embodiment 1 above.

[0087] The computer-readable storage medium can be an internal storage unit of any device or apparatus with data processing capabilities, such as a hard disk or memory, or an external storage device of any device with data processing capabilities, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc.

[0088] Of course, the above description is only a preferred embodiment of the present invention. The present invention is not limited to the above-described embodiments. It should be noted that any equivalent substitutions or obvious modifications made by those skilled in the art under the guidance of this specification fall within the scope of this specification and should be protected by the present invention.

Claims

1. An adaptive fuzzy dynamic self-triggering discrete control method for a permanent magnet synchronous motor, characterized in that, Includes the following steps: Step 1. Establish a discrete model of the permanent magnet synchronous motor; Step 2. Based on the discrete model of the permanent magnet synchronous motor established in Step 1, an adaptive fuzzy dynamic self-triggering discrete controller for the permanent magnet synchronous motor is designed according to the principle of adaptive fuzzy control method and instruction filtering backstepping method; the system state at the current triggering moment is used to predict and calculate the next triggering moment, so as to avoid judging the triggering condition at every sampling moment. Step 3. Using the adaptive fuzzy dynamic self-triggering discrete controller for the permanent magnet synchronous motor designed in Step 2, the permanent magnet synchronous motor can quickly track the desired signal while reducing the computational burden.

2. The adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors according to claim 1, characterized in that, Step 1 specifically involves: Establishing a permanent magnet synchronous motor The coordinate axis model is shown in formula (1): (1) in, Indicates the rotor angular velocity. This represents the moment of inertia of a permanent magnet synchronous motor. This indicates the number of pole pairs in a permanent magnet synchronous motor. and for Stator inductance in coordinate system , They are respectively , Excitation current on the shaft, This refers to the magnetic flux linkage generated by the permanent magnets in a permanent magnet synchronous motor. This represents the friction coefficient of a permanent magnet synchronous motor. This represents the load torque of the permanent magnet synchronous motor. This represents the stator resistance of a permanent magnet synchronous motor. , These are the input signals of the permanent magnet synchronous motor system, representing respectively , Shaft voltage; The permanent magnet synchronous motor model is described using the Euler method as a discrete model as shown in equation (2): (2) in, Indicates the sampling time. and They represent Time and The rotor angular velocity at time t, and They represent Time and time Excitation current on the shaft, and They represent Time and time Excitation current on the shaft, , They represent time , Voltage on the shaft; Define variables , , , , , , , , , , , , , as follows: ， ， ； ， ， ， ； ， ， ， ； ， ， ； The simplified permanent magnet synchronous motor The coordinate axis discrete model is shown in formula (3): (3)。 3. The adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors according to claim 2, characterized in that, Step 2 specifically involves: Step 2.

1. Establish a dynamic self-triggering mechanism; assumed , and These are the three most closely spaced self-triggered moments; The dynamic self-triggering mechanism is established as follows: (4) in, Indicates the first The output of the controller at each self-triggering moment It is the actuator input, i.e., the control law. Self-triggering time It is a subset of traditional sampling sequences. Indicates the first One self-triggered moment; , , It is a positive self-triggering design parameter. This indicates taking the absolute value. This indicates taking the maximum value; ;in , and These are the designed self-triggering parameters, and , It is a systematic error. It is the hyperbolic tangent function; ,in It is the interval between the current self-triggered moment and the previous self-triggered moment. and They represent the first The self-triggered moment and the first The input of a self-triggering executor; when At that time, the dynamic self-triggering mechanism is triggered, updating the control law. Simultaneously calculate the next self-triggering time. ;when At that time, the dynamic self-triggering mechanism was not triggered, and the control law... Maintain the state from the previous self-triggered moment; Based on the above dynamic self-triggering mechanism, the following exists: ,in It is the controller output; Step 2.

2. Construct the system error and error compensation signals; Define the instruction filter as follows: ； in and These are the positive design parameters of the instruction filter. Represents a virtual control law; , This indicates that the input of the filter is The output of the second-order instruction filter at the current step. = , It is an input virtual control law The output signal of the time command filter; Define systematic error , , as follows: (5) in, Preset tracking speed signal; Define error compensation signal , and as follows: (6) in , , This indicates the error after the system error has been compensated by the compensation signal; Step 2.

3. Design an adaptive fuzzy dynamic self-triggering discrete controller for the permanent magnet synchronous motor; According to equations (3), (5), and (6), we get: (7) in This indicates the value of the next step number after the system error has been compensated by the compensation signal. To preset the tracking speed signal, This is the error compensation signal; Selected virtual control law and error compensation signal for: (8) (9) Substituting equations (8) and (9) into equation (7), we get: (10) Choosing Lyapunov functions for: ; According to equation (10), we get First-order difference for: (11) in , It is a positive constant, meaning that the load torque of a permanent magnet synchronous motor system is bounded; According to Young's inequality, we have: (12) Pick According to equations (2), (5), and (6), we get: (13) in ; , Indicates the first The output of the controller at each self-triggering moment is Voltage on the shaft; It is an input virtual control law The output signal of the time command filter, express The value of the signal after compensation in the next step; Fuzzy logic systems exist , making ; in, For the ideal weight column vector, ; To approximate the error, and satisfy... , For any arbitrarily small constant; Represents a fuzzy basis function vector; Weight estimation error , yes The estimated value; make ;in These are positive design parameters; Design a self-triggering control law With Adaptive Law for: (14) (15) in, These are positive design parameters. for The estimated value; According to equations (13) and (14), we get: (16) in Indicates the first Estimates of the ideal weight column vector at each self-triggered moment; Pick According to equations (2), (5), and (6), we get: (17) in ; , Indicates the first The output of the controller at each self-triggering moment is Voltage of the shaft Indicates systematic error. express The value of the signal after compensation in the next step; Fuzzy logic systems exist , making ; in, For the ideal weight column vector, ; To approximate the error, and satisfy... , For any arbitrarily small constant; Represents a fuzzy basis function vector; Weight estimation error , yes The estimated value; Design a self-triggering control law With Adaptive Law for: (18) (19) in, These are positive design parameters. for The estimated value; According to equations (17) and (18), we get: (20) in Indicates the first The estimated value of the ideal weight column vector at each self-triggered moment.

4. The adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors according to claim 1, characterized in that, In step 2, after completing the design of the adaptive fuzzy dynamic self-triggering discrete controller for the permanent magnet synchronous motor, the boundedness analysis of the weighted error value and the system stability analysis are performed on the permanent magnet synchronous motor system controlled by the adaptive fuzzy dynamic self-triggering discrete controller.

5. The adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors according to claim 4, characterized in that, In step 2, the boundedness analysis process for the weighted error values ​​is as follows: The boundedness of the weight error value is proven by considering two cases: one at the self-triggering time. and self-trigger interval time , Indicates the number of trigger intervals. Each sampling time; Based on weighted error value Combining equations (15) and (19), we get: (21) I. At the self-triggered moment Adaptive law renew; Constructing Lyapunov functions for: (22) According to equations (21) and (22), we obtain The first difference is divided into: (23) in, , , , , This indicates a search for the norm. By Young's inequality: (24) according to Substituting equation (24) into equation (23) yields: (25) make Then equation (25) simplifies to: (26) make ,have to: ; ; in , Therefore, the weight error value is bounded at the self-triggering time. II. During the self-trigger interval The adaptive law remains unchanged, and the control signal remains unchanged until the next self-triggering time. The weight error is constant within the self-trigger interval; it only needs to be proven that... It is bounded. This represents the weight estimation error at the first sampling time within the trigger interval; According to equation (26): (27) Equation (27) is further transformed into: (28) ； in, Therefore, the weight error value is also bounded at the self-trigger interval. The system stability analysis process is as follows: III. At the self-triggered moment The system's adaptive law and self-triggering control law are updated; Choosing Lyapunov functions for: (29) in, It is a constant, and , , It is a constant, and ; According to equations (11), (16), (20), and (26), we get: (30) in, , It is a constant. , It is a constant; Select parameters , , , , as well as , so that: , , , ,Right now ,in , It is a constant; therefore Bounded, and further Bounded, because Bounded, obtained Bounded; thus, it is concluded that Bounded; therefore, all signals in a closed-loop system are bounded. IV. During the self-trigger interval The system's adaptive law has not been updated. ; Then the Lyapunov function for: ,get: (31) in , This represents the weight estimate at the self-triggering moment; according to Equations (14), (18), and (28) are transformed into equation (31) using Young's inequality: (32) in: ； , , These are the dynamic self-triggering parameters of the design, where , This represents the weight estimation error value at the self-triggering time; Known weight error value It is bounded at any given moment, therefore It is bounded, that is... ,in , It is a constant; select parameters , , , , , , , ,and ,make , ,but It is bounded; therefore, at the self-trigger interval, all signals in the closed-loop system are bounded.

6. The adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors according to claim 1, characterized in that, In step 2, the designed adaptive fuzzy dynamic self-triggering discrete controller for permanent magnet synchronous motors overcomes the causal contradictions and complexity explosion problems in the traditional backstepping method through the instruction filtering method, and at the same time uses the adaptive fuzzy control method to handle the unknown nonlinear terms of the permanent magnet synchronous motor system.

7. The adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors according to claim 1, characterized in that, In step 3, the process of tracking and controlling the permanent magnet synchronous motor (PMSM) based on a preset reference signal using an adaptive fuzzy dynamic self-triggering discrete controller is as follows: Based on the output of the PSM adaptive fuzzy dynamic self-triggering discrete controller... and The inverter converts the actual output. , , This allows the permanent magnet synchronous motor to maintain good dynamic performance while reducing the computational burden.

8. An adaptive fuzzy dynamic self-triggering discrete control system for a permanent magnet synchronous motor, characterized in that, Includes the following modules: The model building module is used to build discrete models of permanent magnet synchronous motors. The controller design module is used to design an adaptive fuzzy dynamic self-triggering discrete controller for a permanent magnet synchronous motor based on the established discrete model of the permanent magnet synchronous motor, according to the adaptive fuzzy control method and the instruction filtering backstepping method. It uses the system state at the current triggering moment to predict and calculate the next triggering moment, so as to avoid judging the triggering condition at every sampling moment. And a tracking control module, which utilizes the designed adaptive fuzzy dynamic self-triggering discrete controller for permanent magnet synchronous motors to enable permanent magnet synchronous motors to quickly track the desired signal while reducing the computational burden.

9. A computer device comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that, When the processor executes the executable code, it implements the steps of the adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the adaptive fuzzy dynamic self-triggering discrete control method for permanent magnet synchronous motors as described in any one of claims 1 to 7.

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