Permanent magnet synchronous motor vector control system based on fuzzy algorithm and rotating speed ring PI controller online setting method thereof

Abstract

The invention discloses a permanent magnet synchronous motor vector control system based on a fuzzy algorithm and a rotating speed ring PI controller online setting method thereof. According to the method, a vector control system of the PMSM is taken as a research object, a dynamic mathematical model is established aiming at the characteristic that the coupling condition of the PMSM is complex, and vector control is carried out on the PMSM by adopting a rotating speed outer ring and a double-current inner ring. The fuzzy PI controller is applied to the rotating speed ring in the permanent magnet synchronous motor vector control system, so that the parameters of the PI controller are adjusted in real time, the overshoot of the rotating speed of the system is effectively inhibited, and the anti-interference performance and the rotating speed adjusting speed of the system are improved. Meanwhile, in the aspect of torque control, the method improves the response time of the system torque and effectively inhibits the system torque pulsation, so that the adaptive capacity and the dynamic response characteristic of the system are enhanced on the premise that the control precision is not sacrificed.

Description

Technical Field

[0001] The present invention relates to the fields of "motor control technology", "fuzzy algorithm application technology", "adaptive control technology" and the like, and more specifically to a permanent magnet synchronous motor vector control system based on a fuzzy algorithm and an online tuning method for a speed loop PI controller thereof. Background Art

[0002] In the field of permanent magnet synchronous motor (PMSM) control, traditional control methods mainly rely on PID controllers. Although these controllers are simple in structure and easy to implement, they have obvious limitations. First, traditional PID controllers are highly dependent on the mathematical model of the motor. Once there is a deviation in the model, the control effect will be greatly reduced. Secondly, these controllers have poor adaptability when facing complex environmental changes, resulting in long response time and insufficient robustness, making it difficult to meet the needs of high-performance control. In addition, the process of manually adjusting PID parameters is not only cumbersome, but also unable to dynamically adjust the parameters according to the operating status of the motor in real time, which limits the flexibility and accuracy of the control system.

[0003] In order to solve these problems, some researchers have tried to apply artificial intelligence algorithms to the control of PMSM, such as ant colony algorithm, particle swarm algorithm and neural network algorithm. Although these methods can provide better control effects in theory, they usually have huge computational complexity, complex implementation, and high hardware requirements, which is not conducive to promotion in actual industrial applications.

[0004] In contrast, fuzzy algorithm, as an algorithm based on intelligent reasoning, provides a new solution. Fuzzy PID control combines fuzzy algorithm with self-tuning of PID control parameters. It does not rely on the precise mathematical model of the controlled object, can tune parameters online, and exhibits good adaptability and robustness. However, the existing fuzzy PID control method still has certain limitations in practical applications, such as complex parameter tuning process, lack of real-time performance, and room for improvement in dynamic response and anti-disturbance capability. Summary of the invention

[0005] In view of the above problems, this technical solution proposes a method for online tuning of a permanent magnet synchronous motor vector control system and its speed loop PI controller based on fuzzy algorithm. This method takes the vector control system of PMSM as the research object, establishes a dynamic mathematical model for the complex coupling characteristics of PMSM, and uses a speed outer loop and a dual current inner loop to perform vector control on PMSM. By applying the fuzzy PI controller to the speed loop in the permanent magnet synchronous motor vector control system, the real-time adjustment of the PI controller parameters is realized, the overshoot of the system speed is effectively suppressed, and the anti-disturbance performance and speed regulation speed of the system are improved. At the same time, in terms of torque control, this method improves the response time of the system torque and effectively suppresses the system torque pulsation, thereby enhancing the system's adaptive ability and dynamic response characteristics without sacrificing control accuracy.

[0006] The technical solution of the present invention is as follows:

[0007] A permanent magnet synchronous motor vector control system based on fuzzy algorithm and its speed loop PI controller online tuning method,

[0008] The system comprises:

[0009] Permanent magnet synchronous motor, under the control of the controller, realizes the conversion of electrical energy and mechanical energy;

[0010] The controller receives the feedback signal from the sensor and outputs the control signal to the actuator after calculation and processing;

[0011] The sensor converts the measured physical quantity into an electrical signal and transmits it to the controller;

[0012] The actuator converts DC power into AC power according to the output signal of the controller to drive the motor to operate;

[0013] The online tuning method of the speed loop PI controller includes: the speed sensor and the current sensor collect the speed and current signals of the motor in real time and transmit them to the controller; the controller calculates the speed error e and the speed error change rate ec according to the given speed and the feedback speed, and then performs online tuning of the parameters of the speed loop PI controller through the fuzzy algorithm; the tuned PI controller parameters act on the current inner loop to control the output of the inverter, thereby adjusting the speed and current of the motor to achieve high-performance control of the motor. Through the nested structure of the speed outer loop and the double current inner loop, the motor speed and stator current can be accurately controlled.

[0014] Further, the method specifically includes the following steps:

[0015] Step 1: Initialize the system and set the initial parameters of the motor, including rated power, rated speed, stator resistance, direct-axis inductance, quadrature-axis inductance, etc.

[0016] Step 2: Set the speed to 2000r / min and start the motor.

[0017] Step 3: The speed sensor and current sensor collect the speed and current of the motor in real time. The speed error e = (given speed - actual speed), the speed error change rate ec = (current speed error - speed error at the previous moment) / sampling time, and the sampling time is 0.001s.

[0018] Step 4: According to the speed error e and the speed error change rate ec, the parameter increments ΔKp and ΔKi of the PI controller are calculated by the fuzzy algorithm.

[0019] -Assuming that the current speed error e is 200r / min, and the speed error change rate ec is 100r / min / s, after fuzzy reasoning, ΔKp is 5 and ΔKi is 0.5.

[0020] Step 5: Update the parameters of the PI controller, Kp = Kp_initial + ΔKp, Ki = Ki_initial + ΔKi.

[0021] Step 6: The PI controller outputs a control signal to the current inner loop to control the output voltage and frequency of the inverter.

[0022] Step 7: Repeat steps 3 to 6 to adjust the motor's operating status in real time so that the motor speed is stabilized near the given speed and can respond quickly and remain stable under interference conditions such as load changes.

[0023] -For example, when the load suddenly increases, the motor speed decreases, and the speed error increases, the fuzzy algorithm can be used to quickly adjust the PI controller parameters to increase the output torque and quickly restore the motor speed.

[0024] Step 8: The system continues to run, monitoring the operating status and various parameters of the motor to ensure stable and reliable operation of the system.

[0025] Technical effects and advantages of the present invention:

[0026] 1. Improve control accuracy: By combining the radial basis function neural network to perform online identification of the permanent magnet synchronous motor and adjusting the parameters according to the sensitivity information obtained by identification, the accuracy of the control system is improved.

[0027] 2. Enhanced adaptive capability: The fuzzy PID control algorithm does not rely on the precise mathematical model of the controlled object and can adjust parameters online, thus enhancing the adaptive capability of the control system.

[0028] 3. Improved robustness: Compared with the classic PI controller, the fuzzy PID controller has better anti-interference ability and robustness, and can maintain stable control in complex environments.

[0029] 4. Improve dynamic response characteristics: The fuzzy PI controller can adjust the parameters of the PI controller in real time, effectively suppress the overshoot of the system speed, speed up the speed adjustment time, improve the response time of the system torque, and effectively suppress the system torque pulsation.

[0030] 5. Simplified control algorithm: Compared with complex artificial intelligence algorithms, fuzzy algorithms simplify control algorithms, reduce the amount of calculations, and make control systems more efficient.

[0031] 6. Improve system stability: The method of using speed outer loop and dual current inner loop to perform vector control on PMSM improves the stability and control effect of the system. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] Embodiment 1

[0034] The system includes permanent magnet synchronous motor, controller, sensor and actuator.

[0035] 1) Permanent magnet synchronous motor: Select a motor with a rated power of 10kW, a rated speed of 3000r / min, a rated torque of 31.8N·m, a stator resistance of 0.2Ω, a direct-axis inductance of 0.008H, and a quadrature-axis inductance of 0.008H.

[0036] Operating principle: Under the control of the controller, the conversion of electrical energy and mechanical energy is realized.

[0037] 2) Controller

[0038] -Speed ​​outer loop: Fuzzy PI controller is used, and the speed setpoint is set to 2000r / min.

[0039] -Current inner loop: includes direct-axis current inner loop and quadrature-axis current inner loop. The given value of direct-axis current is 0A, and the given value of quadrature-axis current is 20A.

[0040] Operating principle: Receive feedback signals from sensors, and output control signals to actuators after computational processing.

[0041] 3) Sensor

[0042] -Speed ​​sensor: The accuracy is ±1r / min, measuring the actual speed of the motor.

[0043] - Current sensor: with an accuracy of ±0.1A, it measures the direct-axis and quadrature-axis currents of the motor.

[0044] Operating principle: Convert the measured physical quantity into electrical signals and transmit them to the controller.

[0045] 4) Actuator

[0046] - Inverter: Switching frequency is 10kHz, output voltage is 0-380V.

[0047] Operating principle: According to the output signal of the controller, direct current is converted into alternating current to drive the motor to run.

[0048] Embodiment 2

[0049] The online tuning method of the speed loop PI controller includes:

[0050] -Input variables: speed error e and speed error change rate ec. The basic domain of speed error e is [-500,500]r / min, and the basic domain of speed error change rate ec is [-1000,1000]r / min.

[0051] -Output variables: PI controller parameter increments ΔKp and ΔKi. The basic domain of ΔKp is [-10,10], and the basic domain of ΔKi is [-1,1].

[0052] -Fuzzy subsets: The fuzzy subsets of the input variables e and ec and the output variables ΔKp and ΔKi are {NB, NM, NS, ZO, PS, PM, PB}.

[0053] -Membership function: Triangular membership function is used.

[0054] -Fuzzy control rules: Develop a fuzzy control rule table based on experience and experimental data.

[0055] - Defuzzification: Defuzzification is performed using the centroid method.

[0056] Embodiment 3

[0057] After the system is started, the speed sensor and current sensor collect the speed and current signals of the motor in real time and transmit them to the controller. The controller calculates the speed error e and the speed error change rate ec according to the given speed and feedback speed, and then uses the fuzzy algorithm to adjust the parameters of the speed loop PI controller online. The adjusted PI controller parameters act on the current inner loop to control the output of the inverter, thereby adjusting the speed and current of the motor to achieve high-performance control of the motor.

[0058] The specific steps are:

[0059] Step 1: Initialize the system and set the initial parameters of the motor, including rated power, rated speed, stator resistance, direct-axis inductance, quadrature-axis inductance, etc.

[0060] Step 2: Set the speed to 2000r / min and start the motor.

[0061] Step 3: The speed sensor and current sensor collect the speed and current of the motor in real time. The speed error e = (given speed - actual speed), the speed error change rate ec = (current speed error - speed error at the previous moment) / sampling time, and the sampling time is 0.001s.

[0062] Step 4: According to the speed error e and the speed error change rate ec, the parameter increments ΔKp and ΔKi of the PI controller are calculated by the fuzzy algorithm.

[0063] -Assuming that the current speed error e is 200r / min, and the speed error change rate ec is 100r / min / s, after fuzzy reasoning, ΔKp is 5 and ΔKi is 0.5.

[0064] Step 5: Update the parameters of the PI controller, Kp = Kp_initial + ΔKp, Ki = Ki_initial + ΔKi.

[0065] Step 6: The PI controller outputs a control signal to the current inner loop to control the output voltage and frequency of the inverter.

[0066] Step 7: Repeat steps 3 to 6 to adjust the motor's operating status in real time so that the motor speed is stabilized near the given speed and can respond quickly and remain stable under interference conditions such as load changes.

[0067] -For example, when the load suddenly increases, the motor speed decreases, and the speed error increases, the fuzzy algorithm can be used to quickly adjust the PI controller parameters to increase the output torque and quickly restore the motor speed.

[0068] Step 8: The system continues to run, monitoring the operating status and various parameters of the motor to ensure stable and reliable operation of the system.

[0069] Due to the advanced nature of this technical solution, it can be widely used in industrial automation, new energy vehicles, aerospace and other application fields. With the rapid development of industrial automation, the requirements for motor control accuracy and response speed are getting higher and higher. This technical solution can effectively improve the control performance of permanent magnet synchronous motors to meet the needs of industrial automation. At the same time, the development of new energy vehicles has put forward higher requirements on the efficiency and stability of motors. The application of this technical solution can improve the efficiency and stability of motors and promote the development of new energy vehicles. In addition, the aerospace field has extremely high requirements for the reliability and precise control of motors. The application of this technical solution can improve the reliability and control accuracy of motors and promote the development of aerospace technology. Therefore, this technical solution has broad market demand and good application prospects.

[0070] Embodiment 4

[0071] A permanent magnet synchronous motor vector control system based on an adaptive control algorithm and an online tuning method for its speed loop PI controller. The method dynamically adjusts the PID controller parameters through an adaptive control algorithm to adapt to changes in the motor's operating state. The solution includes the following parts: an adaptive control algorithm, a dynamic parameter adjustment mechanism, and a vector control system.

[0072] 1) The adaptive control algorithm dynamically adjusts the proportional, integral and differential parameters of the PID controller according to the real-time operating data of the motor to achieve the optimal control effect.

[0073] 2) The dynamic parameter adjustment mechanism monitors the operating status of the motor in real time and adjusts the PID controller parameters according to the output of the adaptive control algorithm to adapt to different operating conditions.

[0074] 3) The vector control system uses a speed outer loop and a dual current inner loop to control the PMSM, and an adaptive control algorithm is applied to the speed loop to improve the system's anti-disturbance performance and dynamic response characteristics.

[0075] Embodiment 5

[0076] A permanent magnet synchronous motor vector control system based on model predictive control (MPC) and an online tuning method for its speed loop PI controller are proposed. This method achieves precise control of the motor by predicting future system behavior and optimizing control input. The scheme includes the following parts: model predictive control algorithm, optimizer, and vector control system.

[0077] 1) Model predictive control algorithms are based on the dynamic mathematical model of the motor to predict the system behavior over a period of time in the future and optimize the control input to achieve the desired control target.

[0078] 2) The optimizer calculates the optimal control parameters based on the output of the model predictive control algorithm to achieve precise control of the motor.

[0079] 3) The vector control system uses a speed outer loop and a dual current inner loop to control the PMSM, and the model predictive control algorithm is applied to the speed loop to improve the system's anti-disturbance performance and dynamic response characteristics.

[0080] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A permanent magnet synchronous motor vector control system based on fuzzy algorithm and an online tuning method for its speed loop PI controller, characterized in that: The system comprises: Permanent magnet synchronous motor, under the control of the controller, realizes the conversion of electrical energy and mechanical energy; The controller receives the feedback signal from the sensor and outputs the control signal to the actuator after calculation and processing; The sensor converts the measured physical quantity into an electrical signal and transmits it to the controller; The actuator converts DC power into AC power according to the output signal of the controller to drive the motor to operate; The online tuning method of the speed loop PI controller includes: a speed sensor and a current sensor collect the speed and current signals of the motor in real time and transmit them to the controller; the controller calculates the speed error e and the speed error change rate ec according to the given speed and the feedback speed, and then performs online tuning on the parameters of the speed loop PI controller through a fuzzy algorithm; the tuned PI controller parameters act on the current inner loop to control the output of the inverter, thereby adjusting the speed and current of the motor to achieve high-performance control of the motor.

2. According to claim 1, a permanent magnet synchronous motor vector control system based on fuzzy algorithm and an online tuning method for its speed loop PI controller, characterized in that: The following steps are involved: Step 1: Initialize the system and set the initial parameters of the motor; Step 2: Set the speed to 2000r / min and start the motor; Step 3: The speed sensor and current sensor collect the speed and current of the motor in real time. The speed error e = (given speed - actual speed), the speed error change rate ec = (current speed error - previous speed error) / sampling time, and the sampling time is 0.001s; Step 4: According to the speed error e and the speed error change rate ec, the parameter increments ΔKp and ΔKi of the PI controller are calculated by the fuzzy algorithm; Step 5: Update the parameters of the PI controller, Kp = Kp_initial + ΔKp, Ki = Ki_initial + ΔKi; Step 6: The PI controller outputs a control signal to the current inner loop to control the output voltage and frequency of the inverter; Step 7: Repeat steps 3 to 6 to adjust the motor's operating status in real time so that the motor speed is stable near the given speed and can respond quickly and remain stable under interference conditions such as load changes; Step 8: The system continues to run, monitoring the operating status and various parameters of the motor to ensure stable and reliable operation of the system.

3. The method for online tuning of a permanent magnet synchronous motor vector control system and a speed loop PI controller thereof based on a fuzzy algorithm according to claim 2 is characterized in that: The initial parameters include rated power, rated speed, stator resistance, direct-axis inductance, and quadrature-axis inductance.

4. The method for online tuning of a permanent magnet synchronous motor vector control system and a speed loop PI controller thereof based on a fuzzy algorithm according to claim 1 is characterized in that: In the controller, Speed ​​outer loop: adopts fuzzy PI controller, and sets the speed given value to 2000r / min; Current inner loop: includes direct-axis current inner loop and quadrature-axis current inner loop. The given value of direct-axis current is 0A, and the given value of quadrature-axis current is 20A.

5. The method for online tuning of a permanent magnet synchronous motor vector control system and a speed loop PI controller thereof based on a fuzzy algorithm according to claim 1 is characterized in that: The sensor comprises: Speed ​​sensor: The accuracy is ±1r / min, measuring the actual speed of the motor; Current sensor: The accuracy is ±0.1A, measuring the direct-axis and quadrature-axis current of the motor.

6. The method for online tuning of a permanent magnet synchronous motor vector control system and a speed loop PI controller thereof based on a fuzzy algorithm according to claim 1 is characterized in that: The actuator includes an inverter: the switching frequency is 10kHz and the output voltage is 0-380V.

7. The method for online tuning of a permanent magnet synchronous motor vector control system and a speed loop PI controller thereof based on a fuzzy algorithm according to claim 1 is characterized in that: The permanent magnet synchronous motor has a rated power of 10 kW, a rated speed of 3000 r / min, a rated torque of 31.8 N·m, a stator resistance of 0.2 Ω, a direct-axis inductance of 0.008 H, and a quadrature-axis inductance of 0.008 H.

8. The method for online tuning of a permanent magnet synchronous motor vector control system and a speed loop PI controller thereof based on a fuzzy algorithm according to claim 1 is characterized in that: Input variables: speed error e and speed error change rate ec. The basic domain of the speed error e is [-500,500]r / min, and the basic domain of the speed error change rate ec is [-1000,1000]r / min.

9. The method for online tuning of a permanent magnet synchronous motor vector control system and a speed loop PI controller thereof based on a fuzzy algorithm according to claim 1 is characterized in that: Output variables: PI controller parameter increments ΔKp and ΔKi; the basic domain of ΔKp is [-10,10], and the basic domain of ΔKi is [-1,1].