Adaptive nonsingular terminal sliding mode control method for PMSM current loop based on parameter identification

Abstract

The application discloses a PMSM current loop adaptive non-singular terminal sliding mode control method based on parameter identification, which comprises the following steps: step 1, a current loop model of a PMSM in a d-q axis coordinate system is established based on actual control system motor parameter variation or disturbance, and a first-order differential of a q-axis stator current and a first-order differential of a d-axis stator current are obtained; step 2, an ANTSMC is constructed by defining a current loop error, the ANTSMC comprises an adaptive reaching law and a non-singular terminal sliding mode surface, derivatives of the two are obtained in parallel, and a q-axis output voltage and a d-axis output voltage are obtained, the current loop of a permanent magnet synchronous motor field vector control is controlled through the ANTSMC, and the system stability of the ANTSMC is proved through a Lyapunov stability criterion; and step 3, a parameter identifier based on an Adaline neural network is used to identify actual motor parameters of the current loop model of the PMSM and then applied to the ANTSMC.

Description

Technical Field

[0001] This invention relates to the field of current control technology for motor control systems, and in particular to a PMSM current loop adaptive non-singular terminal sliding mode control method based on parameter identification. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs), with their advantages of high power density, high torque-to-inertia ratio, and high efficiency, have been widely used in high-end equipment fields such as electric vehicle drives, CNC machine tool feeds, photoelectric rotary table positioning, and marine electric propulsion. However, the inherent nonlinear characteristics of inverters inevitably couple into the servo control system. These disturbances caused by inverter nonlinearity have a significant negative impact on system control performance: they not only cause stator current waveform distortion and induce torque pulsation, but also significantly reduce the system's trajectory tracking accuracy and operational stability.

[0003] Sliding Mode Control (SMC) is a nonlinear control method derived from variable structure control theory. Its core feature lies in forcing the system state to slide along a preset sliding surface with small amplitude and high frequency by switching the control law, thereby achieving high-precision tracking and robust control of the controlled object. Since its introduction in the 1950s, sliding mode control has been widely and deeply studied and applied in many fields such as motor drive, robot control, power electronics, and aerospace due to its insensitivity to parameter perturbations and external disturbances. However, when permanent magnet synchronous motors operate at high speeds, core parameters such as stator resistance, stator inductance, and permanent magnet flux linkage will change significantly due to temperature variations. These changes directly affect the control accuracy, torque output, and operating efficiency of the motor. At the same time, load changes may also affect system parameters, thus affecting the control effect. Traditional sliding mode control has fixed parameters, making it difficult to simultaneously meet the requirements of chatter suppression and convergence speed. Summary of the Invention

[0004] The purpose of this invention is to address the technical deficiencies in the existing technology by providing an adaptive non-singular terminal sliding mode control method for permanent magnet synchronous motor current loop based on parameter identification, thereby solving the problems of chattering, overshoot, and deterioration of controller performance caused by motor parameter perturbation.

[0005] The technical solution adopted to achieve the purpose of this invention is: An adaptive non-singular terminal sliding mode control method for PMSM current loop based on parameter identification includes the following steps: Step 1: Based on the variation or disturbance of motor parameters in the actual control system, establish a current loop model of the PMSM in the dq-axis coordinate system, and calculate the first derivative of the q-axis stator current. and the first differential of the d-axis stator current ; Step 2, based on the results obtained in Step 1 and The current loop errors of the q-axis and d-axis are calculated, and an adaptive non-singular terminal sliding mode controller (ANTSMC) is constructed based on these errors. The ANTSMC includes an adaptive reaching law and a non-singular terminal sliding surface. The q-axis output voltage is obtained by simultaneously solving the first-order derivatives of the adaptive reaching law for the q-axis and the derivatives of the non-singular terminal sliding surface for the d-axis. and d-axis output voltage The current loop of the permanent magnet synchronous motor's magnetic field vector control is controlled by ANTSMC; Step 3: Use an Adaline neural network-based parameter recognizer to identify the actual motor parameters of the current loop model of the PMSM in Step 1. Apply the identified actual motor parameters to the ANTSMC constructed in Step 2. The actual motor parameters include the q-axis stator inductance. d-axis stator inductance Stator phase resistance and permanent magnet magnetic flux .

[0006] In the above technical solution, in step 1, the current loop model of the PMSM is represented as follows: ; In the formula, The q-axis stator inductance is the value specified for the motor. The first differential of the q-axis stator current. The stator phase resistance of the motor is calibrated. This is the q-axis stator current. This is the q-axis stator voltage. The d-axis stator inductance calibrated for the motor. This represents the number of pole pairs of the motor. The mechanical angular velocity of the motor rotor. The stator current is the d-axis current. The permanent magnet flux linkage calibrated for the motor. The first differential of the d-axis stator current. for d Shaft stator voltage, and Indicates the amount of systematic error; in, and The calculation formula is: ; In the formula, for and The error value, , for and The error value, , for and The error value, , for and The error value, ; and The calculation formula is: .

[0007] In the above technical solution, the expression for the first-order differential of the adaptive reaching law is: ; In the formula, For adaptive reaching law, The first derivative of the adaptive reaching law, It is a function of current error. It is a constant. For the adaptive reaching law, it is a pseudo-exponential window function; in, The calculation formula is: ; In the formula, It is a constant. ; For the q-axis, , Let be the current error function along the q-axis. The calculation formula is: ; In the formula, For q-axis current error, ,in, This is the q-axis reference current. Let be a function of the q-axis current error. It is a constant, and >0, It is a constant, and 0 < <2; Represented as: ; In the formula, It is a constant. It is a constant, and 0 < <1, It is a constant; For the d-axis, , Let be the current error function along the d-axis. The calculation formula is: ; In the formula, For d-axis current error, ,in, The d-axis reference current, It is a function of the d-axis current error; Represented as: .

[0008] In the above technical solution, in step 2, the expression for the non-singular terminal sliding surface of the q-axis is: ; In the formula, It is a non-singular terminal sliding surface along the q-axis. , Let be a constant, where 0 < <1, >0, It is a pseudo-exponential window function that represents the first-order differential of the q-axis current error. in, The calculation formula is: ; In the formula, This is the first derivative of the q-axis current error; The formula for calculating the derivative of the non-singular terminal sliding surface of the q-axis is: ; In the formula, Let be the derivative of the non-singular terminal sliding surface along the q-axis. The first derivative of the q-axis current error. , This is the second derivative of the q-axis current error; in, The calculation formula is: ; In the formula, The first derivative of the q-axis voltage; The calculation formula is: ; In the formula, Let be the second derivative of the q-axis current. The first derivative of the mechanical angular velocity of the motor rotor; The expression for the non-singular terminal sliding surface of the d-axis is: ; In the formula, For the non-singular terminal sliding surface of the d-axis, It is a pseudo-exponential window function that represents the first-order differential of the d-axis current error; in, The calculation formula is: ; In the formula, The first derivative of the d-axis current error; The formula for calculating the derivative of the non-singular terminal sliding surface of the d-axis is: ; In the formula, Let be the derivative of the non-singular terminal sliding surface along the d-axis. Let be the first derivative of the d-axis current error, where , The second derivative of the d-axis current error; The calculation formula is: ; In the formula, The first derivative of the d-axis voltage; The calculation formula is: ; In the formula, The second derivative of the d-axis current. It is the first derivative of the mechanical angular velocity of the motor rotor.

[0009] In the above technical solution, in step 2, let Solve for the q-axis output voltage respectively. and d-axis output voltage , Let be the derivative of the non-singular terminal sliding surface, where ; Among them, the q-axis output voltage The calculation formula is: ; In the formula, The first derivative of the q-axis voltage. t For time, , For adaptive reaching law, It is a non-singular terminal sliding surface. ; d-axis output voltage The calculation formula is: ; In the formula, It is the first derivative of the d-axis voltage.

[0010] In the above technical solution, the stability of ANTSMC is proved using the Lyapunov stability criterion. The specific process is as follows: Define Lyapunov candidate functions ,and ≥0, the derivative of the Lyapunov candidate function is obtained by taking the derivative. Combined with non-singular terminal sliding surfaces judge Is it less than 0, when and The stability of ANTSMC can be proven at that time; The The calculation formula is:

[0011] The for: .

[0012] In the above technical solution, the q-axis stator inductor The identification model is as follows: ; In the formula, For system input, This is the output of the Adaline neural network. For the desired output, For systematic error, The convergence factor is for k+1 The q-axis stator inductance at time t. for k The q-axis stator inductance at time t; The d-axis stator inductor The identification model is as follows: ; In the formula, for k+1 The d-axis stator inductance at time t. for k The d-axis stator inductance at time t; The permanent magnet magnetic chain The identification model is as follows: ; In the formula, for k+1 The permanent magnet flux linkage at any given time, for k The permanent magnet flux linkage at any given moment; The stator phase resistance The identification model is as follows: ; In the formula, for k+1 Stator phase resistance at time t. for k Stator phase resistance at time t.

[0013] A second aspect of the present invention is an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the parameter-identified PMSM current loop adaptive non-singular terminal sliding mode control method.

[0014] A third aspect of the present invention is a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the parameter-identification-based PMSM current loop adaptive non-singular terminal sliding mode control method.

[0015] A fourth aspect of the present invention is a computer program product comprising computer-executable instructions, which, when executed, are used to implement the parameter-identified PMSM current loop adaptive non-singular terminal sliding mode control method.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention addresses the critical shortcomings of traditional exponential sliding mode control (SMC) in simultaneously addressing convergence speed, overshoot, and chatter suppression. It innovatively designs an error-adaptive improved SMC. By introducing a dynamic gain term related to current error to replace the fixed switching gain, it achieves intelligent adjustment: automatically increasing gain to accelerate convergence when far from the sliding surface and decreasing gain to suppress chatter when approaching the sliding surface. Simultaneously, it effectively avoids step response overshoot, resolving the core contradiction of traditional sliding mode control. In establishing the current loop model, the impact of parameter changes on the motor is considered, thus creating a mathematical model of the current loop that takes parameter variations into account. An Adaline neural network is used to identify parameters, which are then applied to the Adaptive Non-Singular Sliding Mode Controller (ANTSMC). This integration of Adaline neural network parameter identification and sliding mode control, through online parameter identification and compensation for parameter changes, enhances the controller's robustness to parameter perturbations and external disturbances. Attached Figure Description

[0017] Figure 1 The diagram shown is a flowchart of the present invention.

[0018] Figure 2 The image shows a performance comparison of different convergence laws.

[0019] Figure 3 The figure shows the simulated waveforms of the q-axis current under different control methods.

[0020] Figure 4 The figure shows the d-axis current simulation waveforms for different control methods.

[0021] Figure 5 The figure shows the simulated waveforms of three-phase current under different control methods.

[0022] Figure 6 The figure shown is a diagram illustrating the parameter identification results of the permanent magnet synchronous motor of the present invention.

[0023] Figure 7 The figure shown is a parameter diagram of the permanent magnet synchronous motor of the present invention. Detailed Implementation

[0024] The present invention will be further described in detail below with reference to specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0025] Example 1 Reference Figure 1 An adaptive non-singular terminal sliding mode control method for PMSM current loop based on parameter identification includes the following steps: Step 1: Based on the changes or disturbances in the motor parameters of the actual control system, establish a current loop model of the PMSM in the dq-axis coordinate system, and calculate the first derivative of the q-axis stator current. and the first differential of the d-axis stator current ; The PMSM current loop model in the orthogonal coordinate system of the dq axes is represented as follows: ; In the formula, It is the q-axis inductance. The first differential of the q-axis stator current. For stator winding resistance, For q-axis current, This is the q-axis stator voltage. For d-axis inductance, This represents the number of pole pairs of the motor. The mechanical angular velocity of the motor rotor. It is a permanent magnet flux linkage. For d-axis inductance, For the electromagnetic torque of the motor, Load torque, It is the first derivative of the mechanical angular velocity of the electronic rotor.

[0026] Furthermore, when the parameters of the actual control system change or are disturbed, the current loop model of the PMSM can be expressed as: ; In the formula, The q-axis stator inductance is the value specified for the motor. The first differential of the q-axis stator current. The stator phase resistance of the motor is calibrated. For q-axis current, This is the q-axis stator voltage. The d-axis stator inductance calibrated for the motor. This represents the number of pole pairs of the motor. The mechanical angular velocity of the motor rotor. The stator current is the d-axis current. The permanent magnet flux linkage calibrated for the motor. The first differential of the d-axis stator current. for d Shaft stator voltage, and Indicates the amount of systematic error; in, and The calculation formula is: ; In the formula, for and The error value, , for and The error value, , for and The error value, , for and The error value, ; but and They are represented as follows: .

[0027] Step 2, based on the results obtained in Step 1 and The current loop errors of the q-axis and d-axis are calculated, and an adaptive non-singular terminal sliding mode controller (ANTSMC) is constructed based on these errors. The ANTSMC includes an adaptive reaching law and a non-singular terminal sliding surface. The q-axis output voltage is obtained by simultaneously solving the first-order derivatives of the adaptive reaching law for the q-axis and the derivatives of the non-singular terminal sliding surface for the d-axis. and d-axis output voltage The current loop of the permanent magnet synchronous motor's magnetic field vector control is controlled by ANTSMC; Furthermore, for the q-axis (i.e., the quadrature axis), the q-axis current loop error is: ; In the formula, For q-axis current error, For reference current, , This is the first derivative of the q-axis current error.

[0028] The adaptive reaching law is calculated based on the current loop error. The expression for the first derivative of the adaptive reaching law is: ; In the formula, For adaptive reaching law, The first derivative of the adaptive reaching law, It is a function of current error. It is a constant. It is a pseudo-exponential window function for the adaptive reaching law.

[0029] Furthermore, The calculation formula is: ; In the formula, It is a constant. .

[0030] Furthermore, , Let be the current error function along the q-axis. The calculation formula is: ; In the formula, For q-axis current error, ,in, This is the q-axis reference current. Let be a function of the q-axis current error. It is a constant, and >0, It is a constant, and 0 < <2; Represented as: ; In the formula, It is a constant. It is a constant, and 0 < <1, It is a constant; The expression for the non-singular terminal sliding surface (NTSM) is: ; In the formula, It is a non-singular terminal sliding surface. It is a non-singular terminal sliding surface along the q-axis. , Let be a constant, where 0 < <1, >0, It is a pseudo-exponential window function that represents the first-order differential of the q-axis current error. The calculation formula is: ; The first derivative of the q-axis current error in the q-axis current loop error Substitute the non-singular terminal sliding surface of the q-axis From this, we can obtain: ; In the formula, , Let be a constant, where 0 < <1, >0, It is a pseudo-exponential window function that represents the first-order differential of the q-axis current error. right Differentiation yields the formula for calculating the derivative of the non-singular terminal sliding surface along the q-axis: ; In the formula, For the derivative of the non-singular terminal sliding surface, Let be the derivative of the non-singular terminal sliding surface along the q-axis. The first derivative of the q-axis current error. , This is the second derivative of the q-axis current error; in, The calculation formula is: ; In the formula, This is the first derivative of the q-axis voltage error. The calculation formula is: ; In the formula, Let be the second derivative of the q-axis current. It is the first derivative of the mechanical angular velocity of the motor rotor.

[0031] Will and Jointly, that is, And let The q-axis output voltage can be obtained. : ; In the formula, The first derivative of the q-axis current loop. The first derivative of the q-axis voltage. , For adaptive reaching law, It is a non-singular terminal sliding surface. .

[0032] Similarly, for the d-axis (i.e., the direct axis), the d-axis current loop error is: ; In the formula, For d-axis current error, For reference current, , It is the first derivative of the d-axis current error.

[0033] For the d-axis, , Let be the current error function along the d-axis. The calculation formula is: ; In the formula, For d-axis current error, ,in, The d-axis reference current, It is a function of the d-axis current error; Represented as: .

[0034] For the d-axis, , For the non-singular terminal sliding surface of the d-axis, The expression is: ; In the formula, It is a pseudo-exponential window function that represents the first-order differential of the d-axis current error; in, The calculation formula is: ; In the formula, The first derivative of the d-axis current error; The formula for calculating the derivative of the non-singular terminal sliding surface of the d-axis is: ; In the formula, Let be the derivative of the non-singular terminal sliding surface along the d-axis. Let be the first derivative of the d-axis current error, where , The second derivative of the d-axis current error; The calculation formula is: ; In the formula, The first derivative of the d-axis voltage; The calculation formula is: ; In the formula, The second derivative of the d-axis current. It is the first derivative of the mechanical angular velocity of the motor rotor.

[0035] make The d-axis output voltage can be obtained. : ; In the formula, It is the first derivative of the d-axis voltage.

[0036] In step 2, the stability of ANTSMC is proven using the Lyapunov stability criterion. The specific process is as follows: Define Lyapunov candidate functions The calculation formula is as follows: ; right Differentiation yields the derivative of the Lyapunov candidate function. for: ; Adaptive reaching law Substitution From this, we can obtain: ; In the formula, since >0, >0, >0, >0, therefore we can conclude: This indicates that the current loop error is constantly approaching 0, proving the stability of ANTSMC.

[0037] Step 3: The parameter recognizer based on the Adaline neural network identifies the actual motor parameters of the current loop model of the PMSM in Step 1, and applies the identified actual motor parameters to the ANTSMC constructed in Step 2. The actual motor parameters include the q-axis stator inductance. d-axis stator inductance, stator phase resistance and permanent magnet magnetic flux .

[0038] The parameter recognizer based on the Adaline neural network identifies the actual parameter values ​​of the motor. , , , Used to observe real-time changes in motor parameters to improve control accuracy, the Adaptive Linear Neuron (Adaline), also known as the adaptive linear neuron, is a single-layer feedforward neural network proposed by Bernard Widrow in 1960. Its core recognition principle is to classify linearly separable patterns or predict continuous values ​​through linear modeling and adaptive weight learning. Essentially, it uses the Least Mean Square (LMS) algorithm to adaptively adjust weights, offering advantages such as simple structure, fast convergence, and low computational complexity. Compared to multi-layer neural networks, Adaline does not require backpropagation; it completes signal fitting only through a linear mapping between the input and output layers, demonstrating unique engineering application value in areas such as online system parameter identification, disturbance compensation, and signal filtering. The specific process of solving its parameters is as follows: Define the output of the Adaline neural network Represented as: ; In the formula, For the weights of the neural network, For system input, For the first The input feature, corresponding to the first... k The input of each neuron, For weights.

[0039] According to the output Calculate the systematic error of an Adaline neural network Represented as: ; In the formula, This is the expected output.

[0040] Due to systematic error The square of the error can be obtained for: ; Taking the minimum mean squared error (MSE) as the optimization objective, the objective function of the Adaline neural network is... for: ; In the formula, It represents the mathematical expectation and reflects the statistical average characteristics of the error.

[0041] Square the error Substitute into the objective function We can obtain: ; Due to the objective function For the weight The function is a quadratic function, therefore it has a unique minimum value. The minimum point can be found using the gradient descent method. Find the weights gradient: ; Let gradient Solving for the optimal weights : ; Due to the statistical characteristics of the expected output and input in a real system ( , Since the mean square error cannot be directly obtained, the square of the instantaneous error is used instead, thus simplifying the gradient. The calculation is as follows: ; Combined with the weight update rule of gradient descent ( ), Let be the convergence factor, 0 < <1, finally yielding the weight recursive formula: ; The actual motor parameters, q-axis stator inductance, are obtained from the PMSM current loop model established in step 1 based on changes or disturbances in the actual control system motor parameters. d-axis stator inductance Stator phase resistance Permanent magnet magnetic flux These are respectively used as system inputs to an Adaline neural network-based parameter recognizer. The q-axis stator inductance is derived. d-axis stator inductance Stator phase resistance Permanent magnet magnetic flux The identification model of the actual parameters of the motor.

[0042] Among them, the q-axis stator inductor The identification model is as follows: ; In the formula, For system input, This is the output of the Adaline neural network. For the desired output, For systematic error, The convergence factor is for k+1 The q-axis stator inductance at time t. for k The q-axis stator inductance at time t; The d-axis stator inductor The identification model is as follows: ; In the formula, for k+1 The d-axis stator inductance at time t. for k The d-axis stator inductance at time t; The permanent magnet magnetic chain The identification model is as follows: ; In the formula, for k+1 The permanent magnet flux linkage at any given time, for k The permanent magnet flux linkage at any given moment; The stator phase resistance The identification model is as follows: ; In the formula, for k+1 Stator phase resistance at time t. for k Stator phase resistance at time t.

[0043] Example 2 To verify the control effect of the ANTSMC based on the Adaline neural network constructed in this invention, the ANTSMC based on the Adaline neural network constructed in Example 1 of this invention was simulated and analyzed using Matlab / Simulink, referring to... Figure 6 , Figure 7 The result of parameter identification is from Figure 6 The quantitative changes in the four curves show that during the dynamic identification process from 0 to 0.5 seconds, the quadrature-axis inductance... Direct-axis inductor Permanent magnet chain and stator resistance All of them rose rapidly within about 0.4 seconds and then tended to a steady state after 0.5 seconds; among them It first shows a transient peak of approximately 8.5 mH before falling back to 5.61 mH. , , They then monotonically or smoothly increased to 5.6 mH, 0.18 Wb, and 0.9, respectively. The steady-state value shows that the fluctuations of each parameter are minimal after reaching steady state, demonstrating the fast convergence speed and high steady-state accuracy of the identification algorithm. The results are consistent with the characteristics of surface-mounted permanent magnet synchronous motors, combined with Figure 7 The accuracy of the identification parameters was verified.

[0044] Reference Figure 2 Among different reaching laws, the adaptive reaching law exhibits superior overall performance in sliding mode control: when far from the sliding surface, its convergence speed approaches that of the exponential reaching law, quickly pulling the system state towards the sliding surface; while when close to the sliding surface, the absolute value of its reaching law decreases smoothly, effectively suppressing chattering and balancing fast convergence with stability. (Refer to...) Figure 3 , Figure 4 , Figure 5It is known that traditional PI control experiences a peak current exceeding 40A during startup, exhibits severe oscillations during regulation, large dynamic overshoot, slow response, and significant current fluctuations after steady state, easily causing motor torque impact and energy loss. ANTSMC's startup overshoot is significantly smaller than traditional PI, and its dynamic response is faster, but some oscillations still exist before steady state, with large current fluctuations. In contrast, the ANTSMC control based on an Adaline neural network in Embodiment 1 of this invention exhibits the smallest startup overshoot, the fastest dynamic response, and almost no current oscillations after steady state. This indicates that Adaline parameter identification significantly improves control accuracy and steady-state performance while retaining the fast convergence advantage of sliding mode control. Therefore, the ANTSMC control based on an Adaline neural network in this invention outperforms both traditional PI control and ANTSMC.

[0045] Example 3 This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the PMSM current loop adaptive non-singular terminal sliding mode control method based on parameter identification as described in Embodiment 1 above.

[0046] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0047] Example 4 This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the PMSM current loop adaptive non-singular terminal sliding mode control method based on parameter identification as described in Embodiment 1 above.

[0048] The computer-readable storage medium in this embodiment can be an internal storage unit of the terminal, such as the terminal's hard disk or memory; the computer-readable storage medium in this embodiment can also be an external storage device of the terminal, such as a plug-in hard disk, smart memory card, secure digital card, flash memory card, etc. equipped on the terminal; furthermore, the computer-readable storage medium can include both the terminal's internal storage unit and external storage devices.

[0049] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0050] Example 5 This embodiment provides a computer program product including computer-executable instructions stored on a machine-readable storage medium (such as a disk, flash memory, or optical disk). When executed by at least one data processing device (such as a microprocessor or digital signal processor), the instructions cause the data processing device to perform the steps in the PMSM current loop adaptive non-singular terminal sliding mode control method based on parameter identification described in Embodiment 1.

[0051] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0052] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0053] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0054] The above description is only a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A PMSM current loop adaptive non-singular terminal sliding mode control method based on parameter identification, characterized in that, Includes the following steps: Step 1: Based on the variation or disturbance of motor parameters in the actual control system, establish a current loop model of the PMSM in the dq-axis coordinate system, and calculate the first derivative of the q-axis stator current. and the first differential of the d-axis stator current ; Step 2, based on the results obtained in Step 1 and The current loop errors of the q-axis and d-axis are calculated, and an adaptive non-singular terminal sliding mode controller (ANTSMC) is constructed based on these errors. The ANTSMC includes an adaptive reaching law and a non-singular terminal sliding surface. The q-axis output voltage is obtained by simultaneously solving the first-order derivatives of the adaptive reaching law for the q-axis and the derivatives of the non-singular terminal sliding surface for the d-axis. and d-axis output voltage The current loop of the permanent magnet synchronous motor's magnetic field vector control is controlled by ANTSMC; Step 3: Use an Adaline neural network-based parameter recognizer to identify the actual motor parameters of the current loop model of the PMSM in Step 1. Apply the identified actual motor parameters to the ANTSMC constructed in Step 2. The actual motor parameters include the q-axis stator inductance. d-axis stator inductance Stator phase resistance and permanent magnet magnetic flux .

2. The PMSM current loop adaptive non-singular terminal sliding mode control method based on parameter identification as described in claim 1, characterized in that, In step 1, the current loop model of the PMSM is represented as follows: ； In the formula, The q-axis stator inductance is the value specified for the motor. The first differential of the q-axis stator current. The stator phase resistance of the motor is calibrated. This is the q-axis stator current. This is the q-axis stator voltage. The d-axis stator inductance calibrated for the motor. This represents the number of pole pairs of the motor. The mechanical angular velocity of the motor rotor. The stator current is the d-axis current. The permanent magnet flux linkage calibrated for the motor. The first differential of the d-axis stator current. for d Shaft stator voltage, and Indicates the amount of systematic error; in, and The calculation formula is: ； In the formula, for and The error value, , for and The error value, , for and The error value, , for and The error value, ; and The calculation formula is: 。 3. The PMSM current loop adaptive non-singular terminal sliding mode control method based on parameter identification as described in claim 2, characterized in that, In step 2, the expression for the first derivative of the adaptive reaching law is: ； In the formula, For adaptive reaching law, The first derivative of the adaptive reaching law, It is a function of current error. It is a constant. For the adaptive reaching law, it is a pseudo-exponential window function; in, The calculation formula is: ； In the formula, It is a constant. ; For the q-axis, , Let be the current error function along the q-axis. The calculation formula is: ； In the formula, For q-axis current error, ,in, This is the q-axis reference current. Let be a function of the q-axis current error. It is a constant, and >0, It is a constant, and 0 < <2; Represented as: ； In the formula, It is a constant. It is a constant, and 0 < <1, It is a constant; For the d-axis, , Let be the current error function along the d-axis. The calculation formula is: ； In the formula, For d-axis current error, ,in, The d-axis reference current, It is a function of the d-axis current error; Represented as: 。 4. The PMSM current loop adaptive non-singular terminal sliding mode control method based on parameter identification as described in claim 3, characterized in that, In step 2, the expression for the non-singular terminal sliding surface of the q-axis is: ； In the formula, It is a non-singular terminal sliding surface along the q-axis. , Let be a constant, where 0 < <1, >0, It is a pseudo-exponential window function that represents the first-order differential of the q-axis current error. in, The calculation formula is: ； In the formula, This is the first derivative of the q-axis current error; The formula for calculating the derivative of the non-singular terminal sliding surface of the q-axis is: ； In the formula, Let be the derivative of the non-singular terminal sliding surface along the q-axis. The first derivative of the q-axis current error. , This is the second derivative of the q-axis current error; in, The calculation formula is: ； In the formula, The first derivative of the q-axis voltage; The calculation formula is: ； In the formula, Let be the second derivative of the q-axis current. The first derivative of the mechanical angular velocity of the motor rotor; The expression for the non-singular terminal sliding surface of the d-axis is: ； In the formula, For the non-singular terminal sliding surface of the d-axis, It is a pseudo-exponential window function that represents the first-order differential of the d-axis current error; in, The calculation formula is: ； In the formula, The first derivative of the d-axis current error; The formula for calculating the derivative of the non-singular terminal sliding surface of the d-axis is: ； In the formula, Let be the derivative of the non-singular terminal sliding surface along the d-axis. Let be the first derivative of the d-axis current error, where , The second derivative of the d-axis current error; The calculation formula is: ； In the formula, The first derivative of the d-axis voltage; The calculation formula is: ； In the formula, The second derivative of the d-axis current. It is the first derivative of the mechanical angular velocity of the motor rotor.

5. The PMSM current loop adaptive non-singular terminal sliding mode control method based on parameter identification as described in claim 4, characterized in that, In step 2, let Solve for the q-axis output voltage respectively. and d-axis output voltage , Let be the derivative of the non-singular terminal sliding surface, where ; Among them, the q-axis output voltage The calculation formula is: ； In the formula, The first derivative of the q-axis voltage. t For time, , For adaptive reaching law, It is a non-singular terminal sliding surface. ; d-axis output voltage The calculation formula is: ； In the formula, It is the first derivative of the d-axis voltage.

6. The PMSM current loop adaptive non-singular terminal sliding mode control method based on parameter identification as described in claim 1, characterized in that, In step 2, the stability of ANTSMC is proven using the Lyapunov stability criterion. The specific process is as follows: Define Lyapunov candidate functions ,and ≥0, the derivative of the Lyapunov candidate function is obtained by taking the derivative. Combined with non-singular terminal sliding surfaces judge Is it less than 0, when and This proves that ANTSMC is stable; The The calculation formula is: ； The The calculation formula is: 。 7. The PMSM current loop adaptive non-singular terminal sliding mode control method based on parameter identification as described in claim 1, characterized in that, The q-axis stator inductor The identification model is as follows: ； In the formula, For system input, This is the output of the Adaline neural network. For the desired output, For systematic error, The convergence factor is for k+1 The q-axis stator inductance at time t. for k The q-axis stator inductance at time t; The d-axis stator inductor The identification model is as follows: ； In the formula, for k+1 The d-axis stator inductance at time t. for k The d-axis stator inductance at time t; The permanent magnet magnetic chain The identification model is as follows: ； In the formula, for k+1 The permanent magnet flux linkage at any given time, for k The permanent magnet flux linkage at any given moment; The stator phase resistance The identification model is as follows: ； In the formula, for k+1 Stator phase resistance at time t. for k Stator phase resistance at time t.

8. An electronic device, characterized in that, include: One or more processors; A memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the parameter identification-based PMSM current loop adaptive non-singular terminal sliding mode control method as described in claim 1.

9. A computer-readable storage medium, characterized in that, It stores computer-executable instructions, which, when executed, are used to implement the PMSM current loop adaptive non-singular terminal sliding mode control method based on parameter identification as described in claim 1.

10. A computer program product, characterized in that, The aforementioned computer program product includes computer-executable instructions that, when executed, implement the PMSM current loop adaptive non-singular terminal sliding mode control method based on parameter identification as described in claim 1.

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