基于粒子群算法的液压APC系统分数阶PID控制器设计  

Optimal fractional order PID control of hydraulic APC system based on particle swarm optimization

作　　者：魏立新[1] 王浩[1] 王铁兴 

机构地区：[1]燕山大学电气工程学院,河北秦皇岛066004 [2]秦皇岛市自来水有限公司,河北秦皇岛066000

出　　处：《燕山大学学报》2017年第3期246-252,共7页Journal of Yanshan University

基　　金：河北省自然科学基金资助项目(F2016203249);河北省高等学校创新团队领军人才培育计划资助项目(LJRC013)

摘　　要：为提高冷轧液压伺服位置控制系统在复杂工况下的暂态性能和稳定性,提出一种径向基函数神经网络在线自适应调节分数阶PID控制算法。为提高网络精度,减少冗余隐层节点,采用带有2次变异机制的粒子群算法离线同时优化网络结构和初始参数,同时选择BP算法在线调整网络参数,使FOPID控制系统具备良好的自适应能力。仿真结果表明,该控制系统能够快速准确跟随输入信号,且能明显抑制外在干扰和系统参数扰动,控制效果优于其他对比控制算法。For controlling the complicated nonlinear system effectively,the fractional order PID neural network controller is proposed based on particle swarm（PSO） algorithm in this paper.The controller,combined with fractional order PID control and BP neural network and PSO algorithm,uses the BP neural network to set the fractional order PID controller’s parameters online and utilize PSO algorithm with two variation mechanism instead of back propagation algorithm in network training,to avoid the slow convergence speed,falling into local optimum easily and computing complex faults.The result of simulation shows that the fractional order PID neural network controller based on PSO algorithm could control the nonlinear systems effectively,moreover,control effect is better than that of neural network adaptive PID controller and neural network adaptive PID controller based on PSO optimization BP PID.

关 键 词：液压伺服控制系统 分数阶PID 粒子群算法 网络优化 

分 类 号：TM46[电气工程—电器]