不确定时变系统的鲁棒学习控制算法

研究不确定性时变系统在有限时间区间上重复作业和在无限时间区间上周期作业的跟踪控制问题. 基于Lyapunov-like方法, 给出了形式简单的鲁棒迭代学习控制和鲁棒重复控制两种算法. 两种学习算法均可弥补单一控制算法的缺陷, 鲁棒控制部分被用来保证闭环系统中所有变量的有界性, 学习控制部分可有效消除系统跟踪误差, 改善系统的跟踪性能. 仿真结果验证了两种学习算法的有效性.

Robust learning control algorithms for uncertain time-varying systems

The trajectory tracking problem of uncertain time-varying systems is addressed, where the same tasks are performed repeatedly within a finite duration of time, or periodic references are followed over an infinite interval. Through the Lyapunov-like synthesis, two robust learning control algorithms are developed based on the control tasks, and their stability and convergence results are established. Both algorithms can compensate for the shortcoming when either one is applied separately. The robust control component guarantees all the variables in the closed-loop to be bounded, while the learning control component ensures that the tracking error converges to zero. Numerical results are presented to demonstrate effectiveness of the proposed learning algorithms.