混沌优化Smith预估补偿器

一个控制系统的Smith预估补偿器参数如果选取不精确可能会导致控制器失效,针对这一问题,提出一种在线混沌优化Smith预估补偿器参数的方法.首先通过混沌映射对被控对象的阶跃响应曲线进行数学辨识得到其传递函数,然后依据辨识出的数学模型进行离线PID参数优化.当模型变化时,控制器可以自适应调节参数.此方法具有被控对象自动识别、系统针对性强、可以在线滚动优化等优点.分别以低阶和高阶数学模型为被控对象进行仿真,仿真结果表明,控制器具有较强的跟踪能力和鲁棒性.

Chaotic Optimization of Smith Predictor

A controller may be failure if the parameters of a Smith predictor are not precisely selected. Therefore， a method is proposed by which the parameters can be chaotically optimized on-line. The transfer function of the plant is identified first by chaotic mapping of the step response curve， then， the PID parameters are optimized off-line by using the identified model. When the model is changed， the parameters of the controller can be adjusted adaptively. The plant can be automatically identified， and rolling optimization process is performed in the method. The control system simulation for the plant is carried out， and the results show that the tracking performance and robustness of the controller are satisfactory.