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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。
Aiming at the time delay and strong nonlinearity of pneumatic loading servo control system, a control scheme of improved sliding mode interference observer (ISMDO) based on chaos particle swarm optimization (CPSO) is proposed. The estimated output of the observer is used to estimate the actual output, and the estimated output with no delay is calculated. The error between the output value and the set value is used as the input of the sliding mode controller to calculate the control amount. At the same time, particle swarm optimization algorithm is used to optimize the parameters of the controller. In order to make the optimization better, the particle swarm optimization algorithm is first selected by using the chaos inverse learning method, the primary particle, the reusable particle elimination algorithm, And based on chaos system replacement particle strategy to supplement the group. Compared with PID control algorithm, sliding mode disturbance observer (SMDO) and other control strategies, the system simulation of step and sinusoidal signals is compared. The results show that the algorithm can better solve the system delay and nonlinearity. The experimental results show that this algorithm has better control performance for pneumatic loading system.