Optimization method of PSO-PID control for interferometric closed-loop fiber optic gyroscope

被引:0
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作者
Liu, Shangbo [1 ]
Dan, Zesheng [1 ]
Lian, Baowang [1 ]
Xu, Jintao [2 ]
Cao, Hui [3 ]
机构
[1] School of Electronics and Information, Northwestern Polytechnical University, Xi'an,710072, China
[2] Xi'an Zhongke Huaxin Measurement and Control Co., Ltd., Xi'an,710100, China
[3] School of Automation, Northwestern Polytechnical University, Xi'an,710072, China
关键词
Aircraft control - Bandwidth - Closed loop control systems - Controllers - Electric control equipment - Errors - Fiber optics - Fibers - Fuzzy inference - Fuzzy neural networks - Gyroscopes - Inertial navigation systems - Locks (fasteners) - Military applications - Particle swarm optimization (PSO) - Proportional control systems - Tuning;
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摘要
Objective PSO-PID control optimization algorithm based on the interferometric closed-loop fiber optic gyroscope has been widely used in military and civil fields, such as aerospace, defense equipment, navigation survey, vehicle inertial navigation system and other industrial systems. These applications are developing in the direction of lightness, low power consumption, long life, high reliability, no self-locking and mass production. PSO-PID controller can improve the dynamic response of fiber optic gyroscope and effectively track the angular rate input of fiber optic gyroscope. Fiber optic gyroscope is based on Sagnac effect in closed optical path, so its bandwidth is much larger than that of traditional gyroscope. In digital closed-loop fiber optic gyroscope, the response speed of optical path is very fast, and the system bandwidth is mainly determined by the detection circuit. Choosing a suitable digital controller is helpful to improve the dynamic performance of fiber optic gyroscope. Methods The system block diagram of fiber optic gyroscope (Fig.1) is established, and the ICFOG closed-loop system is equivalent to a mathematical model (Fig.2) by analyzing the working principle of fiber optic gyroscope, and finally the closed-loop discrete control system is deduced. On this basis, a new PSO-PID compound controller is designed (Fig.3), and the optimization algorithm steps of PID controller of standard PSO are analyzed (Fig.4). The controller can adjust parameters Kp, Ki and Kd online during operation (Fig.15). At the same time, by comparing with the PID parameter tuning method of BP neural network (Fig.5), fuzzy PID parameter tuning method (Fig.6) and PID control method, the advantages of PSO-PID control are illustrated by comparing the angular rate input tracking speed of fiber optic gyro (Fig.12) and the angular rate input tracking error of fiber optic gyro (Fig.13). Results and Discussions Using PSO-PID control method, it is found that the fitness value changes rapidly. When the number of iterations is 15, the fitness value can reach the optimal solution, and the optimal solution is 21.892 5. At the same time, the tracking time of FOG angular rate input is 1.2 s. Compared with BP-PID, PID, and F-PID control methods, the tracking speed is increased by 1.91, 3.5 and 1.75 times respectively. After the PSO-PID control method, the tracking error is 4.7 × 104 m, which is smaller than other control methods. Compared with F-PID, BP-PID and PID control methods, its control accuracy is improved by 45.27%, 46.03% and 66.30% respectively. According to the comparison of dynamic performance of different control methods (Tab.1), it is known that PSO-PID controller can achieve the control goal quickly and has a small tracking error. Conclusions Based on the mathematical model of fiber optic gyro, this paper puts forward an optimization scheme of fiber optic gyro digital controller. The traditional digital controller is improved, and the PSO-PID controller is proposed and simulated. Compared with many control methods, the simulation results show that PSO-PID controller can shorten the adjustment time and reduce overshoot, thus effectively improving the dynamic performance of fiber optic gyroscope on the premise of ensuring stability, and has important engineering significance and practical value. To apply this optimization scheme to engineering practice, more external factors and more detailed control parameter analysis need to be considered, which will be the focus of later research. © 2024 Chinese Society of Astronautics. All rights reserved.
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