Abstract : Due to the high-order, nonlinear, and strongly coupled characteristics of the controlled objects in the field of electric drives, it is difficult to achieve precise control using traditional PID control methods. To address this deficiency, this paper designs a single-neuron PID controller with adaptive and self-learning functions and applies it to a vector control system for a permanent magnet synchronous motor. To examine the control effect of the system, simulation studies were conducted using MATLAB/SIMULINK simulation software. Simulation results show that the speed control system using single-neuron control has better starting performance. When the load or motor parameters change abruptly, the system exhibits short recovery time and small overshoot, demonstrating strong adaptability and robustness, proving that the designed controller is an effective controller. Keywords : permanent magnet synchronous motor, adaptive control, single neuron CLC classification number: TM351, TP273 Document identification code: A [align=center]The Research on PMSM's Control System Based on Single Neuron PID Controller FengYu DingHong Ludong University, Yantai, China 264025[/align] Abstract : The traditional control method of PID is very difficult to meet the need of high reliability in electric drive field as the controlled object has characteristics of higher order, nonlinear and coupling. Aiming at the shortcomings, The PMSM's vector control system based on single neuron PID controller which has the capability of self-studying and self-adapting was designed in the paper. Simulation experiments were done to check its characteristic in circumstances of MATLAB/SIMULINK. The results show that the system has better start-up performance which can reach stability faster with less oscillation under the occurrence of parameter or load variations, and reveals strong adaptation and rubness, These confirm that the single neural control system works well. Keywords： Permanent magnet synchronous motor (PMSM); Adaptive Control; Single Neuron 1. Introduction Traditional PID control, due to its mature technology, has been widely used in motor control. However, in high-performance speed control systems, the precise parameters of the controlled motor are often difficult to obtain, and these parameters change during actual operation. Because PID parameters are not easily adjusted online, conventional PID control often cannot adapt to parameter changes and nonlinear characteristics of the controlled object, making it difficult to achieve satisfactory control results. In recent years, research on neural networks has become increasingly active and has begun to be applied in the field of electrical drives. However, due to the complex structure and slow convergence speed of neural networks, practical applications are difficult. Neurons, as the basic building blocks of neural networks, possess self-learning and adaptive capabilities, do not rely on precise mathematical models of the controlled object, and have a simple structure, making real-time control easy. Utilizing this characteristic of neurons, a single neuron speed regulator can be formed by combining single neuron control with PID control, achieving good control effects against uncertainties such as changing objects and random disturbances. This paper designs a single neuron PID control system for a permanent magnet synchronous motor based on the principle of single neuron PID control and combines it with vector control concepts. Simulation experiments were conducted in the Matlab/Simulink software environment. Simulation results show that under this control method, the response speed and anti-interference ability of the permanent magnet synchronous motor speed regulation system are significantly improved, and the designed controller can effectively make up for the shortcomings of the PID controller. 2. Single Neuron Adaptive PID Control 2.1 Single Neuron PID Control Principle The model of the single neuron is shown in Figure 1. It is a multi-input single-output nonlinear processing unit with self-learning ability, where x[sub]i[/sub] , ω[sub]i[/sub] (i = 1 , 2 , n) are the input quantity and corresponding weight (also known as connection weight coefficient) of the controller, respectively; K is the proportional coefficient; f (•) is the activation function of the neuron. In a neural network that implements adaptive PID control using a single neuron, there are three input signals: x1(k), x2(k), and x3(k). Let the error between the actual output and the desired output be e(k). Then: In the above formula, k is the proportional coefficient of the single-neuron controller; x1(k), x2(k), and x3(k) correspond to the integral, proportional, and derivative components of the PID controller, respectively, while the weight coefficients ω1, ω2, and ω3 correspond to the proportional, integral, and derivative coefficients, respectively. [align=center] Figure 1 Artificial Neuron Model[/align] 2.2 Learning Algorithm The neuron's ability to respond to external signals is a key characteristic. A crucial feature of neurons is their ability to continuously learn and adapt their acquired knowledge structure to changes in the surrounding environment. The learning of neurons is carried out by modifying their own weight coefficients according to a certain performance index. The paper adopts the supervised Hebb learning algorithm. The learning process of its neurons is as follows: z(k) - the neuron output error signal, which is the system performance index z(k) = e(k). η - neuron learning rate. Generally, the learning rate should not be too large, otherwise the neuron regulator is prone to overshoot; the learning rate should not be too small, otherwise the neuron regulator adjustment process is slow. In order to ensure the convergence of the learning algorithm and the robustness of the control, after normalization, the formula can be obtained: Through the above analysis, it can be seen that after adopting a certain learning rule, the single neuron controller can automatically adjust the neuron weight coefficients to adapt to the state change of the controlled object. Its function is equivalent to a variable coefficient adaptive controller. At this time, the dynamic characteristics of the system can only depend on the error signal and are less affected by the change of model parameters [1]. 3. Design of a vector control system for a permanent magnet motor using a single neuron In the traditional control system, the speed regulator generally adopts a PID regulator. Its input is the speed feedback value and the given value, and the output result should be the torque given value. Since the system uses id=0 control, the torque and current amplitudes are proportional, so the output of the speed regulator is actually the given value of the current amplitude (DC). The traditional dual closed-loop control system is already very mature in theory and engineering practice. Using this control method, the speed loop of the speed regulation system is changed to use single neuron control. Since the system parameters contained in the current loop have clear values ​​and do not change much during system operation, and considering the speed of the current loop, the inner loop control method remains unchanged. The principle block diagram of the speed regulation system is shown in Figure 2. [align=center] Figure 2 Permanent magnet motor speed regulation system based on single neuron controller[/align] The converter in the figure is used to obtain the three input quantities x[sub]1[/sub](k), x[sub]2[/sub](k) and x[sub]3[/sub](k) of the single neuron. Here: the expressions of x[sub]2[/sub](k) and x[sub]3[/sub](k) are as shown in equation (1). 4. Simulation Analysis and Results 4.1 Establishment of Simulation Model The simulation uses a permanent magnet motor with an external rotor structure. The simulation model of the vector control system with id=0 is shown in Figure 3: [align=center] Figure 3 PMSM Vector Control System Model Based on Single Neuron Controller[/align] The main technical indicators of the permanent magnet synchronous motor used in the simulation are: P[sub]N[/sub] = 1.7 kW , J =0. 0267 kg•m[sup]2[/sup] , P = 2 , R [sub]r[/sub]= 1. 804 Ω , L [sub]d[/sub] = L[sub]q[/sub]= 5. 5 mH . In the simulation model, since the single neuron controller cannot be directly described by the transfer function, the program can be written using the S-function module provided by MATLAB and the single neuron controller can be put into operation. S-Function is a programming language description of dynamic systems. It can be written in C language or Matlab language. This paper uses Matlab language to write a single neuron S-Function based on the Hebb learning algorithm, and thus establishes a Simulink simulation model of a single neuron PID controller. 4.2 Simulation results In the design of a single neuron adaptive PID controller, the key is to determine the adjustable parameters K, ηP, ηI, ηD, and the initial value of the weighting coefficients. Among these parameters, K is the most sensitive parameter of the system. The larger the value of K, the better the speed, but the overshoot increases. Too large a value of K will cause system oscillation or even instability; the smaller the value of K, the smaller the overshoot, but the speed becomes worse and the system response is too slow. Therefore, the selection of K requires repeated experiments, which also brings certain difficulties to the system design. Therefore, the reference [2] proposes a control strategy that also learns K online at the same time, which can achieve a better control effect. Since a normalized learning algorithm is adopted, the output of the controller is mainly determined by wi(k), so the learning efficiency has little impact on the control characteristics. As long as the learning rate is set to an arbitrary initial value before the controller is put into operation, it is necessary to ensure that its value is between 0 and 1. The initial value of the weighting coefficient can be determined by initializing it using the neural network toolbox provided by Simulink in MATLAB, or by calculating it using the empirical formula proposed in reference [3]. The above parameters are finally determined as follows: In order to compare the control performance of the single neuron PID controller with that of the PID controller, a PID controller was also designed. In practical applications, the differential term of the PID control method plays a role in suppressing overshoot in the startup phase, while in the steady state phase, since the differential term is more sensitive to the interference signal, it affects the steady state performance of the system, so it is generally not desirable for it to play a role. At the same time, the neuron has the ability to learn, and by appropriately adjusting the weighting coefficients of the first two terms, the purpose of reducing overshoot can be achieved in the startup phase, so the differential term is usually ignored. This can simplify the algorithm, save storage space and shorten the execution time of the algorithm, and improve the real-time performance of the system [1]. The PID controller parameters are set as P=50, I=1.8, D=0. Simulation results are as follows: [align=center]1) Starting performance analysis of the motor under no-load conditions with a speed step of 600 r/min: Figure 6 Disturbance immunity analysis under load changes[/align] 5. Conclusion The simulation results show that when using single-neuron PID control, the motor can quickly reach steady state during startup and reduces speed overshoot, making the startup process smoother and faster. When sudden load increases or parameter changes occur, the fluctuations in the speed and torque response curves are smaller, and the recovery time is shorter, demonstrating strong adaptability and robustness. Therefore, combining single-neuron and PID to form a single-neuron adaptive regulator can effectively improve control performance and system performance. References [1] Wan Jianru, Zhang Haibo, Cao Caikai. Speed ​​regulation system of permanent magnet synchronous motor based on single neuron PID controller [J]. Power Electronics Technology, 2005, 39 (1): 75-78. [2] Ding Jun, Xu Yongmao. Single neuron adaptive PID controller and its application [J]. Control Engineering, 2004, 11 (1): 27-30. [3] Chen Jing, Wang Yongji. Research on temperature control system based on single neuron adaptive PID control [J]. Computer Technology and Automation, 2006, 25 (1): 20-22. [4] Sun Zengqi. Intelligent control theory and technology [M]. Beijing: Tsinghua University Press, 2003. [5] Shen Anwen, Shu Zhou, Wang Yongji. AC speed regulation system based on single neuron control and its simulation [J]. Journal of Three Gorges University (Natural Science Edition), 2002, 24 (3): 26-28. [6] Zhang Jianxia, ​​Yang Yong, Xu Dezhi. AC speed regulation system with single-neuron adaptive PID control [J]. Journal of Electrical Machines and Control, 2007, 11(2): 130-133. About the author: Feng Yu, female, lecturer, research interests include power electronics and electric drives. Contact information: School of Physics and Electronic Engineering, Ludong University, 13220935041, E-mail: [email protected]