气动加载系统的粒子群-滑模观测器控制  

Particle Swarm Optimization-Sliding Mode Observer control for pneumatic control loading

作　　者：白国振[1] 邱磊 周媛[1] 杨磊[1] 

机构地区：[1]上海理工大学机械学院,上海200093

出　　处：《控制工程》2017年第11期2324-2331,共8页Control Engineering of China

基　　金：上海市自然科学基金项目(13ZR458500)

摘　　要：针对气动加载伺服控制系统的时滞、强非线性,提出一种基于混沌粒子群(CPSO)的改进滑模干扰观测器(ISMDO)的控制方案。利用观测器预估理论对实际输出进行估计,计算出无延时的预估输出,并将此输出值与设定值的误差作为滑模控制器的输入计算控制量。同时采用粒子群算法进行控制器的参数寻优,为使寻优效果更好,首先采用混沌反学习法"初选"粒子,再利用"淘汰"条件对粒子群算法进行筛选,并基于混沌系统替换粒子策略对群体进行补充。通过与PID控制算法,滑模干扰观测器(SMDO)等不同控制策略对阶跃、正弦信号的系统仿真进行比较,证明算法能较好的解决系统的延迟和非线性。并通过试验验证对于气动加载系统来说,该算法具有较好的控制性能。To solve the problems of time-delay and strong non-linear in the pneumatic servo control loading system,this paper proposes an improved sliding mode disturbance observer （ISMDO） based on chaotic particle swarm optimization （CPSO）.The ISMDO uses the forecast theory to estimate the actual output,which can calculate the estimated output without delay, then using the error between the estimated value and the set value as the input of the sliding mode controller to calculate controlled quantity.And particle swarm optimization （pso） algorithm is adopted to improve the optimization parameters of the controller.To make the optimization effect better, this paper uses the chaos opposition-based learning method to select the primary particles, after that,using the ＂elimination＂ conditions on the screening of particle swarm optimization,and basing on the strategy of using chaotic system to replace particle for the group replenished.Comparing with PID control algorithm, the sliding mode disturbance observer （SMDO） on the step,sine signal simulation,this control method is proved that it can solve the delay and nonlinear system better. And through the test,for pneumatic loading system,the algorithm＇s control has good performance.

关 键 词：图像分割 气动加载伺服控制系统 粒子群 滑模干扰观测器 混沌系统 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]