一种改进的粒子群算法的分数阶控制研究  

Research on Fractional Order Control of an Improved Particle Swarm Optimization Algorithm

作　　者：兰歆 韦宏利[1] 陈超波[1] LAN Xin;WEI Hong-li;CHEN Chao-bo(School of Electronic and Information Engineering,Xi爷an Technological University,Xi爷an 710021,China)

机构地区：[1]西安工业大学电子信息工程学院,陕西西安710021

出　　处：《计算机技术与发展》2018年第10期145-149,共5页Computer Technology and Development

基　　金：陕西省国际科技合作计划项目(2017KW-009);陕西省教育科研计划项目(16JF013)

摘　　要：分数阶PID控制器在工程应用中出现了控制参数多且难以整定等问题。针对这些问题,提出一种改进的粒子群分数阶控制算法,并在单级倒立摆控制系统中进行实验验证。该算法将混沌算法与惯性权重调整的粒子群算法相融合,对粒子群进行混沌初始化,并将陷入局部最优的粒子进行混沌搜索,既优化了惯性权重非线性调整方法来提高算法的收敛精度,又得到了全局最优解。实验结果表明,CAPSO算法在分数阶控制器的参数整定方面优于主导极点法、粒子群优化(PSO)等算法。与PSO算法相比,具有收敛速度快、超调量小、稳定性好、抗干扰性强等特点;经CAPSO算法优化的分数阶控制器动态响应特性要优于整数阶PID控制器。The fractional PID controller exists too many control parameters and difficult parameter tuning and other problems in engineering. Therefore,we propose a fractional order control algorithm called chaotic adaptive particle swarm optimization （CAPSO）,which isverified by the single inverted pendulum control system. This algorithm combines the chaos algorithm with inertia weight adjustment ofthe particle swarm algorithm. After the chaotic initialization of particle swarm,it exerts chaotic search on the swarms falling into the localoptimal particle,not only optimizing nonlinear inertia weight adjustment method to improve its convergence accuracy,but also getting theglobal optimum solution. The experiment shows that the CAPSO is better than the dominant pole method and PSO in the parameter tuning of the fractional order controller. Compared with PSO,it has many merits of the fast convergence speed,small overshoot,strong stability and anti-interference. The dynamic response of the system with fractional order controller optimized by CAPSO is better than thatwith integer order PID controller.

关 键 词：分数阶 混沌算法 粒子群优化 自适应控制 惯性权重 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]