Permanent magnet synchronous motor adaptive control method and system based on PLC

Abstract

The invention relates to the technical field of motor control, in particular to a PLC-based permanent magnet synchronous motor self-adaptive control method and system, state space modeling is carried out on a permanent magnet synchronous motor system based on the PLC to obtain a state space equation, a Luenberger state observer is designed, a variable structure control strategy is introduced, a switching surface and a switching rule are selected, and the adaptive control of a permanent magnet synchronous motor is realized. A variable structure Luenberger observer is constructed to estimate the rotor position of a motor system, the rotor position is estimated, the output of the observer is used as the input of PLC control, the parameters of a controller are dynamically adjusted according to the actual operation condition and the estimated state, and the permanent magnet synchronous motor is adaptively controlled through the PLC, so that the sensor cost can be reduced; the system reliability and the control precision are improved, the control algorithm is simplified, the system dynamic change and different working areas can be adapted through the variable structure Luenberger observer, and the robustness of external disturbance and model uncertainty is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of motor control, and in particular to a PLC-based permanent magnet synchronous motor adaptive control method and system. Background Art

[0002] Permanent magnet synchronous motor (PMSM) is increasingly used in defense, aerospace, electric vehicle drive and other fields due to its various advantages. With the development of science and technology and social level, the high reliability and high stability of motor control systems have become the focus of attention in many fields. However, when the motor is running in a variety of complex and changeable application scenarios such as long-term operation, temperature changes, load changes, etc., the relevant parameters of the motor will change, which will also cause a certain degree of degradation of the controller performance. In the process of mechanical parameter identification, the traditional sliding mode observer (SMO) has problems such as jitter and slow response, and additional filters are required to obtain more accurate identification results, which may cause delays and amplitude attenuation of the identification results. Therefore, a more accurate and stable motor adaptive control method and system are needed. Summary of the invention

[0003] In view of this, an object of the present invention is to propose a PLC-based permanent magnet synchronous motor adaptive control method and system to solve the problem of insufficient accuracy and stability of existing motor control methods.

[0004] Based on the above purpose, the present invention provides a PLC-based permanent magnet synchronous motor adaptive control method, the method comprising: S1. Perform state space modeling on the permanent magnet synchronous motor system based on PLC, and express the permanent magnet synchronous motor system as a state space equation based on PLC; S2. Design the Lumberg state observer based on the state space equation of the permanent magnet synchronous motor system; S3, introduce variable structure control strategy, select switching surface and switching law, construct variable structure Lumberg observer to estimate the rotor position of the motor system; S4, based on the PLC sensor to detect the actual position of the permanent magnet synchronous motor system rotor, using the PLL locking circuit, by comparing the difference between the predicted position and the actual position, and adjusting the control signal, so that the predicted position is consistent with the actual position; S5. Select the control strategy of the neural network adaptive motor model, use the output of the observer as the input of the PLC control, dynamically adjust the parameters of the controller according to the actual operating conditions and the estimated state, and perform adaptive control of the permanent magnet synchronous motor through the PLC.

[0005] Preferably, in step S1, the state space equation based on PLC is a mathematical model of the permanent magnet synchronous motor in a synchronous rotating dq coordinate system: ; u d ,u q with u a ,u b ,u c The transformation relationship is: ; Among them, u a ,u b and u c is the three-phase stator voltage of the permanent magnet synchronous motor, R s is the stator resistance, is the single-phase flux amplitude of the rotor permanent magnet, is the electrical position of the rotor, ω m is the rotor mechanical angular velocity, n p is the number of magnetic pole pairs, τ e is the electromagnetic torque, τ1 is the load torque, B is the damping coefficient, u d and u q is the stator voltage of the dq axis, i d and i q is the stator current of the dq axis, L d and L q is the inductance of the dq axis, J represents the torque coefficient, u α Permanent magnet synchronous motor α Phase voltage, u β Permanent magnet synchronous motor β Phase voltage, T 2s / 2r represents Clarke transform, T 3s / 2r Represents Park transform.

[0006] Preferably, the mathematical model of the permanent magnet synchronous motor in the synchronous rotating dq coordinate system is obtained by reducing and decoupling the mathematical model of the permanent magnet synchronous motor in the three-phase stationary coordinate system through coordinate transformation, and the mathematical model in the three-phase stationary coordinate system is: ; Among them, i a ,i b and i c is the three-phase stator current, ω e is the electrical angular velocity of the rotor.

[0007] Preferably, the coordinate transformation includes: Clarke transformation is performed to convert the abc three-phase stationary coordinate system into the α-β two-phase stationary coordinate system; Perform Park transformation to transform the α-β two-phase stationary coordinate system into a rotating dq coordinate system; The AC component in the three-phase stationary coordinate system is obtained by inverse transformation, and the coordinate system is transformed into: .

[0008] Preferably, constructing a variable structure Lumberg observer comprises: Design a basic Lumberg observer with the following structure: ; Where x represents the state variable, u is the control input, and y is the control output. Represents the estimated state, represents the estimated output, represents the first-order derivative of the estimated state, A is the state matrix, B is the input matrix, L is the observer gain matrix, C is the output matrix, and D is the feedback gain matrix; The variable structure control strategy is introduced, and the switching surface is selected as the state error, which is expressed as: ; Where e is the estimated error of the state; Design a sliding mode control law near the switching surface to ensure the convergence of the state estimation; The sliding mode control strategy is introduced into the observer design to obtain the variable structure observer.

[0009] Preferably, the formula of the sliding mode control law is: ; Where s is the sliding mode variable and λ>0 is the gain parameter.

[0010] Preferably, the variable structure observer is: ; where μ(s) is the variable structure control term, i.e. the switching control law: ; α and β are design parameters used to ensure the effectiveness of switching control.

[0011] Preferably, the control strategy of the neural network adaptive motor model includes: Obtain the input and output data of the motor under different working conditions through experiments or simulations; Standardize input and output data; Divide the standardized data into training set, validation set and test set; The mean square error is selected as the loss function to measure the gap between the predicted value and the actual value; Select gradient descent or Adam optimizer as the optimization algorithm; The neural network is trained using the training set and fine-tuned using the validation set until the network's verification accuracy on the validation set exceeds the set value, and the optimization result is output as a control signal.

[0012] The present invention also provides a PLC-based permanent magnet synchronous motor adaptive control method and system, including a strategy module, a PLC control module and a PLC information acquisition module: The strategy module is used to perform state space modeling on the permanent magnet synchronous motor system based on PLC, and express the permanent magnet synchronous motor system as a state space equation based on PLC; according to the state space equation of the permanent magnet synchronous motor system, a Lumberg state observer is designed; The variable structure control strategy is introduced, the switching surface and switching law are selected, and the variable structure Lumberg observer is constructed to estimate the rotor position of the motor system. The actual position of the rotor of the permanent magnet synchronous motor system is detected by the PLC information acquisition module, and the PLL locking circuit is used to compare the difference between the predicted position and the actual position and adjust the control signal so that the predicted position is consistent with the actual position; The control strategy of the neural network adaptive motor model is selected, and the output of the observer is used as the input of the PLC control. The PLC control module dynamically adjusts the parameters of the controller according to the actual operating conditions and the estimated state, and the permanent magnet synchronous motor is adaptively controlled through the PLC.

[0013] Beneficial effects of the present invention: The present invention obtains state space equations by performing state space modeling on a permanent magnet synchronous motor system, designs a Lumberg state observer, introduces a variable structure control strategy, selects a switching surface and a switching rule, and constructs a variable structure Lumberg observer to estimate the rotor position of the motor system. By estimating the rotor position, the motor is adaptively controlled, which can reduce sensor costs, improve system reliability, improve control accuracy, and simplify control algorithms; By designing a variable structure Lumberg observer, we can adapt to the dynamic changes of the system and different working areas, and improve the robustness of the system to external disturbances and model uncertainties. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 A schematic diagram of a flow chart of a method for adaptively controlling a permanent magnet synchronous motor according to an embodiment of the present invention; Figure 2 Schematic diagram of the control strategy of the neural network adaptive motor model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0017] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0018] like Figure 1 As shown, the embodiment of this specification provides a PLC-based permanent magnet synchronous motor adaptive control method and system, including the following steps: S1. Perform state space modeling of the permanent magnet synchronous motor system based on PLC, and represent the permanent magnet synchronous motor system as a state space equation based on PLC, including determining the state variables, input and output of the system, and the mathematical form of the state equation and the output equation.

[0019] S2. According to the state space equation of the permanent magnet synchronous motor system, the Lumberg state observer is designed. The state observer is usually an additional system with the same or similar dynamic characteristics as the actual system, which is used to estimate the state variables of the system from the output data of the motor. Then, the appropriate observer gain is designed according to the system model and observer type to ensure the accuracy and stability of the state estimation.

[0020] S3, introduce variable structure control strategy, select switching surface and switching law, construct variable structure Lumberg observer to estimate the rotor position of the motor system; S4, based on the PLC sensor to detect the actual position of the permanent magnet synchronous motor system rotor, using the PLL locking circuit, by comparing the difference between the predicted position and the actual position, and adjusting the control signal, so that the predicted position is consistent with the actual position; S5. Select the control strategy of the neural network adaptive motor model, use the output of the observer as the input of the controller, and dynamically adjust the parameters of the controller according to the actual operating conditions and estimated states to cope with the changes and uncertainties of the system and realize the adaptive control of the permanent magnet synchronous motor.

[0021] As an implementation mode, in step S1, the state space equation is a mathematical model of the permanent magnet synchronous motor in a synchronous rotating dq coordinate system: ; u d ,u q with u a ,u b ,u c The transformation relationship is: ; Among them, u a ,u b and u c is the three-phase stator voltage of the permanent magnet synchronous motor, R s is the stator resistance, φf is the single-phase flux amplitude of the rotor permanent magnet, θe is the electrical position of the rotor, ω m is the rotor mechanical angular velocity, n p is the number of magnetic pole pairs, τ e is the electromagnetic torque, τ1 is the load torque, B is the damping coefficient, u d and u q is the stator voltage of the dq axis, i d and i q is the stator current of the dq axis, L d and L q is the inductance of the dq axis, T 2s / 2r represents Clarke transform, T 3s / 2r Represents Park transform.

[0022] As an implementation method, the mathematical model of the permanent magnet synchronous motor in the synchronous rotating dq coordinate system is obtained by reducing and decoupling the mathematical model of the permanent magnet synchronous motor in the three-phase stationary coordinate system through coordinate transformation. The mathematical model in the three-phase stationary coordinate system is: ; Among them, i a ,i b and i c is the three-phase stator current, ω e is the electrical angular velocity of the rotor, L a , L b and L c They respectively represent the three-phase inductance of the permanent magnet synchronous motor.

[0023] As an implementation method, the coordinate transformation includes: Clarke transformation is performed to convert the abc three-phase stationary coordinate system into the α-β two-phase stationary coordinate system; Perform Park transformation to transform the α-β two-phase stationary coordinate system into a rotating dq coordinate system; The AC component in the three-phase stationary coordinate system is obtained by inverse transformation, and the coordinate system is transformed into: .

[0024] As an implementation method, constructing a variable structure Lumberg observer includes: Design a basic Lumberg observer with the following structure: ; Where x represents the state variable, u is the control input, and y is the control output. Represents the estimated state, represents the estimated output, represents the first-order derivative of the estimated state, A is the state matrix, B is the input matrix, L is the observer gain matrix, C is the output matrix, and D is the feedback gain matrix; The corresponding state space model of the controlled system is: .

[0025] In order to improve the robustness of the observer, a variable structure control strategy is introduced. The key to designing a variable structure observer is to select a suitable switching surface and switching law, including: The variable structure control strategy is introduced, and the switching surface is selected as the state error, which is expressed as: ; Where e is the estimated error of the state; The sliding mode control law is designed near the switching surface to ensure the convergence of the state estimation. The formula of the sliding mode control law is: ; Where s is the sliding mode variable and λ>0 is the gain parameter.

[0026] The sliding mode control strategy is introduced into the observer design to obtain the variable structure observer, which is: ; Where μ(s) is the variable structure control term, i.e. the switching control law: ; α and β are design parameters used to ensure the effectiveness of switching control.

[0027] As an implementation method, a control strategy of a neural network adaptive motor model, such as Figure 2 As shown, including: Obtain the input and output data of the motor under different working conditions through experiments or simulations; Standardize input and output data; Divide the standardized data into training set, validation set and test set; The mean square error is selected as the loss function to measure the gap between the predicted value and the actual value; Select gradient descent or Adam optimizer as the optimization algorithm; The neural network is trained using the training set and fine-tuned using the validation set until the network's verification accuracy on the validation set exceeds the set value, and the optimization result is output as a control signal.

[0028] This method introduces the sliding mode control law to ensure that the error converges to the sliding surface, further ensuring the stability of the state estimation. The Lyapunov method can also be used to verify the convergence of the state estimation and its robustness to external disturbances.

[0029] Finally, the performance of the designed variable structure Lumberg observer is verified through numerical simulation, and the error between the estimated result and the actual state is compared to ensure the robustness and stability of the system under different working conditions.

[0030] The present invention also provides a PLC-based permanent magnet synchronous motor adaptive control system, including a strategy module, a PLC control module and a PLC information acquisition module: The strategy module is used to perform state space modeling on the permanent magnet synchronous motor system based on PLC, and express the permanent magnet synchronous motor system as a state space equation based on PLC; According to the state space equation of permanent magnet synchronous motor system, the Lumberg state observer is designed; The variable structure control strategy is introduced, the switching surface and switching law are selected, and the variable structure Lumberg observer is constructed to estimate the rotor position of the motor system. The actual position of the rotor of the permanent magnet synchronous motor system is detected by the PLC information acquisition module, and the PLL locking circuit is used to compare the difference between the predicted position and the actual position and adjust the control signal so that the predicted position is consistent with the actual position; The control strategy of the neural network adaptive motor model is selected, and the output of the observer is used as the input of the PLC control. The PLC control module dynamically adjusts the parameters of the controller according to the actual operating conditions and the estimated state, and the permanent magnet synchronous motor is adaptively controlled through the PLC.

[0031] Those skilled in the art should understand that the discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present invention is limited to these examples; under the concept of the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes in different aspects of the present invention as described above, which are not provided in detail for the sake of simplicity. Any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A PLC-based permanent magnet synchronous motor adaptive control method, characterized in that: The method comprises: S1. Perform state space modeling on the permanent magnet synchronous motor system based on PLC, and express the permanent magnet synchronous motor system as a state space equation based on PLC; S2. Design the Lumberg state observer based on the state space equation of the permanent magnet synchronous motor system; S3, introduce variable structure control strategy, select switching surface and switching law, construct variable structure Lumberg observer to estimate the rotor position of the motor system; S4, based on the PLC sensor to detect the actual position of the permanent magnet synchronous motor system rotor, using the PLL locking circuit, by comparing the difference between the predicted position and the actual position, and adjusting the control signal, so that the predicted position is consistent with the actual position; S5. Select the control strategy of the neural network adaptive motor model, use the output of the observer as the input of the PLC control, dynamically adjust the parameters of the controller according to the actual operating conditions and the estimated state, and perform adaptive control of the permanent magnet synchronous motor through the PLC.

2. The PLC-based permanent magnet synchronous motor adaptive control method according to claim 1 is characterized in that: In step S1, the PLC-based state space equation is a mathematical model of the permanent magnet synchronous motor in a synchronous rotating dq coordinate system: ； u d ,u q with u a ,u b ,u c The transformation relationship is: ； Among them, u a ,u b and u c is the three-phase stator voltage of the permanent magnet synchronous motor, R s is the stator resistance, is the single-phase flux amplitude of the rotor permanent magnet, is the electrical position of the rotor, ω m is the rotor mechanical angular velocity, n p is the number of magnetic pole pairs, τ e is the electromagnetic torque, τ1 is the load torque, B is the damping coefficient, u d and u q is the stator voltage of the dq axis, i d and i q is the stator current of the dq axis, L d and L q is the inductance of the dq axis, J represents the torque coefficient, u α Permanent magnet synchronous motor α Phase voltage, u β Permanent magnet synchronous motor β Phase voltage, T 2s / 2r represents Clarke transform, T 3s / 2r Represents Park transform.

3. The PLC-based permanent magnet synchronous motor adaptive control method according to claim 2 is characterized in that: The mathematical model of the permanent magnet synchronous motor in the synchronous rotating dq coordinate system is obtained by reducing and decoupling the mathematical model of the permanent magnet synchronous motor in the three-phase stationary coordinate system through coordinate transformation. The mathematical model in the three-phase stationary coordinate system is: ； Among them, i a ,i b and i c is the three-phase stator current, ω e is the electrical angular velocity of the rotor, L a , L b and L c They represent the three-phase inductance of the permanent magnet synchronous motor respectively.

4. The PLC-based permanent magnet synchronous motor adaptive control method according to claim 3 is characterized in that: The coordinate transformation includes: Clarke transformation is performed to convert the abc three-phase stationary coordinate system into the α-β two-phase stationary coordinate system; Perform Park transformation to transform the α-β two-phase stationary coordinate system into a rotating dq coordinate system; The AC component in the three-phase stationary coordinate system is obtained by inverse transformation, and the coordinate system is transformed into: 。 5. The PLC-based permanent magnet synchronous motor adaptive control method according to claim 1, characterized in that: The variable structure Lumberg observer comprises: Design a basic Lumberg observer with the following structure: ; Where x represents the state variable, u is the control input, and y is the control output. Represents the estimated state, represents the estimated output, represents the first-order derivative of the estimated state, A is the state matrix, B is the input matrix, L is the observer gain matrix, C is the output matrix, and D is the feedback gain matrix; The variable structure control strategy is introduced, and the switching surface is selected as the state error, which is expressed as: ; Where e is the estimated error of the state; Design a sliding mode control law near the switching surface to ensure the convergence of the state estimation; The sliding mode control strategy is introduced into the observer design to obtain the variable structure observer.

6. The PLC-based permanent magnet synchronous motor adaptive control method according to claim 5, characterized in that: The formula of the sliding mode control law is: ; Where s is the sliding mode variable and λ>0 is the gain parameter.

7. The PLC-based permanent magnet synchronous motor adaptive control method according to claim 6, characterized in that: The variable structure observer is: ; where μ(s) is the variable structure control term, i.e. the switching control law: ; α and β are design parameters used to ensure the effectiveness of switching control.

8. The PLC-based permanent magnet synchronous motor adaptive control method according to claim 1, characterized in that: The control strategy of the neural network adaptive motor model includes: Obtain the input and output data of the motor under different working conditions through experiments or simulations; Standardize input and output data; Divide the standardized data into training set, validation set and test set; The mean square error is selected as the loss function to measure the gap between the predicted value and the actual value; Select gradient descent or Adam optimizer as the optimization algorithm; The neural network is trained using the training set and optimized using the validation set until the network's verification accuracy on the validation set exceeds the set value, and the optimization result is output as a control signal.

9. A PLC-based permanent magnet synchronous motor adaptive control system, characterized in that: The system includes a strategy module, a PLC control module and a PLC information acquisition module: The strategy module is used to perform state space modeling on the permanent magnet synchronous motor system based on PLC, and express the permanent magnet synchronous motor system as a state space equation based on PLC; According to the state space equation of permanent magnet synchronous motor system, the Lumberg state observer is designed; The variable structure control strategy is introduced, the switching surface and switching law are selected, and the variable structure Lumberg observer is constructed to estimate the rotor position of the motor system. The actual position of the rotor of the permanent magnet synchronous motor system is detected by the PLC information acquisition module, and the PLL locking circuit is used to compare the difference between the predicted position and the actual position and adjust the control signal so that the predicted position is consistent with the actual position; The control strategy of the neural network adaptive motor model is selected, and the output of the observer is used as the input of the PLC control. The PLC control module dynamically adjusts the parameters of the controller according to the actual operating conditions and the estimated state, and the permanent magnet synchronous motor is adaptively controlled through the PLC.

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