Abstract: With the increase in the speed and acceleration of the target being measured, higher and higher requirements are placed on the rapid acquisition capability of the opto-electronic tracking servo system. The classical control method cannot fully meet the engineering needs. This paper designs a scale factor self-tuning two-dimensional fuzzy controller and adds it to the opto-electronic tracking servo system. Simulation results show that the dynamic performance of the servo system is greatly improved. Keywords : fuzzy control; scale factor self-tuning; dynamic performance Introduction: In recent years, fuzzy control technology has been widely applied to various fields of production and life. It is favored by industry professionals for its accurate mathematical model of the controlled object, good adaptability, good system robustness and easy implementation of overshoot-free control[1]. In particular, the two-dimensional fuzzy controller is highly regarded for its relatively simple design and high control accuracy. Based on the classical control method, this paper adds a proportional factor self-adjusting two-dimensional fuzzy controller to form a servo control system model. The switching between the classical control method and the fuzzy controller is performed by writing the S function of the M file. The simulation results show that the dynamic performance of the photoelectric tracking servo system is greatly improved. I. Mathematical Model of Photoelectric Tracking Servo System The photoelectric tracking servo system belongs to the double closed-loop single-input single-output position follower system. The inner loop is the velocity loop and the outer loop is the position loop. The control object in this paper is the photoelectric tracking system turntable. Its transfer function is: The controllers of the velocity loop and the position loop are designed using the lead-lag compensation method. The main components of the closed-loop system can be found in the reference[2], which will not be detailed here. II. Fuzzy Controller Design The Control System Toolbox is a collection of functions and tools in the MATLAB software package specifically designed for control system engineering. This toolbox provides a rich set of algorithms for designing, analyzing, and modeling general control systems. SIMULINK is an interactive environment for modeling, analyzing, and simulating various dynamic systems. Through the rich functional blocks provided by SIMULINK, dynamic system models can be created quickly. The Fuzzy Logic Toolbox utilizes fuzzy logic-based system design tools and a GUI to complete the entire process of designing fuzzy control inference systems. It uses simple fuzzy rules to model complex system behaviors and then applies these rules to the fuzzy inference system. S-functions are a powerful programming mechanism provided by SIMULINK, allowing users to implement their own algorithms. 1. Design and Selection of Fuzzy Control Input Variables: The fuzzy controller in the system adopts a dual-input, single-output controller. The input variables are the deviation signal E and the deviation rate of change EC. The output variable is the control quantity U. The quantization universes of E, EC, and U are all (-6 6), and the fuzzy subsets are all {NB, NM, NS, ZO, PS, PM, PB}. Typing the FUZZY command in the MATLAB main interface command window will enter the fuzzy controller's graphical user interface FIS editor, where membership functions for E, EC, and U can be established respectively. Here, the triangular (trimf) membership function is selected. 2. Establishment of Fuzzy Control Rules There are two methods for establishing fuzzy control rules: empirical induction and inductive synthesis. This paper uses empirical induction. The establishment of fuzzy control rules follows these principles: When the deviation is large in the positive direction and the change in deviation is also large in the positive direction, the output of the control quantity U should be large in the positive direction; when the deviation is small in the positive direction or zero and the change in error is also small in the positive direction or zero, the output of the control quantity U should be small in the positive direction or zero; when the deviation is small in the negative direction and the change in error is small, the output of the control quantity U should be small in the negative direction; when the deviation is large in the negative direction and the change in deviation is also large in the negative direction, the output of the control quantity U should be large in the negative direction; ... Design fuzzy control rules in the FIS editor, as shown in Table 1: [align=center]Table 1 Fuzzy control rule table of opto-electronic tracking servo system[/align] III. Introduction to the simulation model of opto-electronic tracking servo system with two-dimensional fuzzy controller: As shown in Figure 1, the opto-electronic tracking servo system is a dual-loop servo system, consisting of a velocity loop and a position loop. In the position loop, the fuzzy controller and the conventional classical controller are designed to perform segmented control according to the size of the system deviation. [align=center] Fig.1 simulation model of SIMULINK of opto-electronic tracking servo system with self-tuning two-dimensional fuzzy controller[/align] [align=center] Fig.2 simulation model of SIMULINK of Subsystem1[/align] The Subsystem1 is shown in Fig.2. The deviation E and EC are the two inputs of the S-function. The output of the S-function is the scaling factor Ke, Kec and Ku, as well as the input jd of the position loop conventional controller. The S-function ep11_he.m can realize the switching between the two controllers. The switching point is selected as 0.1. When the absolute value of the system deviation is greater than the switching point, the fuzzy controller works, which makes the system deviation decrease rapidly; when it is less than the switching point, the jd port output of the Subsystem1 subsystem is e, and the conventional controller works to ensure the system control accuracy[3]. ep11_he.m configures the scaling factors Ke and Kec of the two inputs E and EC of the fuzzy controller. Since the values ​​of the proportional factors Ke and Kec have a great influence on the dynamic performance of the photoelectric tracking servo control system. When Ke is larger, the overshoot of the system is also larger, the transition process is longer, but the rise time is shorter; when Ke is larger, the overshoot of the system is smaller, but the response speed of the system is slower. At the same time, if the output proportional factor Ku of the fuzzy controller is too small, the dynamic process will be longer, and if it is too large, the system will oscillate [4]. According to this rule (verified by simulation test), in the S-function ep11_he.m, the two inputs E and EC are specifically linked to the two input proportional factors Ke and Kec respectively, so that Ke and Kec change with the changes of E and EC. When the deviation is large, Ke takes a larger value, the rise time of the system is shorter, and the response speed is faster; when the deviation is small, Ke should be a smaller value, so that the overshoot of the system decreases; similarly, the deviation change can be linked with EC, and a larger value can be maintained in the rise time of the system response to reduce the overshoot of the system; when EC is small, the value of Kec decreases rapidly, and the system maintains a faster response speed. In ep11_he.m, the principle for selecting the value of Ku is to take a larger value during the rising state of the system response to reduce the dynamic process time of the system; when the system E and EC are relatively small, Ku takes a smaller value to avoid system oscillation. IV. Result Analysis: After simulation, the step response curves of the conventional controller (Fig. 3) and the opto-electronic tracking servo system with a two-dimensional fuzzy controller (Fig. 4 and Fig. 5) are obtained as follows: [align=center] Fig. 3 step response of classic control of opto-electronic tracking servo system[/align] [align=center] Fig. 4 step response of opto-electronic tracking servo system with two-dimensional fuzzy control[/align] [align=center] Fig. 5 step response of opto-electronic tracking servo system with self-tuning two-dimensional fuzzy control[/align] Comparing the step response curves of the photoelectric tracking servo system with a self-adjusting factor (Figure 5), the classical controller, and the general two-dimensional fuzzy controller (Figures 3 and 4), it can be concluded that the self-adjusting factor two-dimensional fuzzy controller has a smaller overshoot (0.6%) and a smaller settling time (0.04 seconds to ±5%) compared to the classical controller (which has a larger overshoot, exceeding 15%, and a settling time of more than 0.4 seconds to ±5%) and the general two-dimensional fuzzy controller (which has an overshoot of 1.2% and a settling time of approximately 0.3 seconds to ±5%). Simulations show that the photoelectric tracking servo system with the self-adjusting factor two-dimensional fuzzy controller has better dynamic performance. The innovation of this paper lies in its use of an S-function to integrate the switching between the two controllers in the photoelectric tracking servo system and the self-adjustment of the fuzzy controller's proportional factor. Furthermore, the dynamic performance of the photoelectric tracking servo system is improved by simultaneously adjusting the three two-dimensional fuzzy controller factors: Ke, Kec, and Ku. References: [1] Liao Jingsheng. Fuzzy control DC speed regulation system based on TMS320LF2407 [J]. Microcomputer Information, 2005. Vol.21. No.4 [2] Wang Jianli. Research on TV tracking and acquisition technology of photoelectric theodolite [D]. Doctoral dissertation of Graduate School of Chinese Academy of Sciences, 2002. [3] Han Xiaoquan. Research on the application of fuzzy control in photoelectric tracking servo system [D]. Doctoral dissertation of Graduate School of Chinese Academy of Sciences, 2005. [4] Zhu Jing. Fuzzy control principle and application [M]. Beijing: Machinery Industry Press, 1995.