一类非线性系统的基于时间序列的多模型自适应控制

Multiple Models Adaptive Control Based on Time Series for a Class of Nonlinear Systems

针对一类非线性离散时间系统,提出了一种基于时间序列的多模型自适应控制器(Multiple models adaptive controller,MMAC).该控制器首先利用聚类方法建立多个线性固定模型,然后,利用系统的时间序列和方向导数建立一个反映工作点变化趋势的局部加权模型,在此基础上增加了一个全局自适应模型和一个可重新赋值的自适应模型,并设计了一个切换机构选择最优模型实现控制.仿真结果表明该控制器不但具有良好的暂态性能、较快的控制速度,而且在相似的控制效果下,可以极大地减少模型的数量.

For a class of nonlinear discrete time systems,a multiple models adaptive controller（MMAC） based on time series is proposed.It uses clustering method to establish some linear local models,utilizes time series and directional derivative to establish a weighted model to approximate the real system when its working point jumps abruptly,and adds one global adaptive model with a re-initialized adaptive model to get the multiple models.Then a switching mechanism is designed to select the optimal controller to realize the control.Finally,in the simulation result,it can be seen that the proposed controller not only improves the transient response and speed up the control effect,but also reduces the number of the multiple model sets greatly,especially for a similar control response.