基于改进量子遗传算法的超声电机模糊PID控制  

Fuzzy PID control of ultrasonic motor based on improved quantum genetic algorithm

作　　者：冯建鑫[1] 王强[1] 王雅雷 胥彪[1] FENG Jian-xin;WANG Qiang;WANG Ya-lei;XU Biao(Academy of Astronautics,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)

机构地区：[1]南京航空航天大学航天学院,南京210016

出　　处：《吉林大学学报（工学版）》2021年第6期1990-1996,共7页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(61603183);南京航空航天大学研究生创新基地(实验室)开放基金项目(kfjj20201502)。

摘　　要：针对超声电机非线性、时变性的特点,设计了模糊自整定PID控制器,并利用量子遗传算法对模糊自整定PID控制器参数进行优化,以提高系统的动态性能和适应性。针对传统量子遗传算法的不足,对编码方式、种群初始化、量子旋转门、量子变异以及增加量子灾变5个方面进行改进。仿真结果表明:改进量子遗传算法改善了传统量子遗传算法容易产生种群早熟的问题,提高了算法收敛性能。同时,基于改进量子遗传算法的模糊自整定PID控制器与经典的模糊自整定PID控制器相比,明显提高了超声电机系统的动态和稳态性能。Aiming at the problem of the nonlinear and time-varying characteristics of ultrasonic motor, a fuzzy self-tuning PID controller is designed, and the parameters of the fuzzy self-tuning PID controller are optimized by using an improved quantum genetic algorithm, which can improve the dynamic performance and adaptability of the system. In order to overcome the shortcomings of traditional quantum genetic algorithm, five improving measures are taken, including the coding mode, population initialization,quantum rotation gate, quantum mutation and adding quantum catastrophe. The simulation results show that the improved quantum genetic algorithm can improve the convergence performance and the premature population problem of traditional quantum genetic algorithm. At the same time, the fuzzy self-tuning PID controller based on the improved quantum genetic algorithm significantly improves the dynamic and stable state performance of the ultrasonic motor system compared with the classical fuzzy self-tuning PID controller.

关 键 词：控制理论与控制工程 超声电机 模糊PID控制 改进量子遗传算法 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]