不确定性系统的鲁棒迭代学习控制及其应用

This project has investigated the optimal iterative learning control algorithms, the iterative learning control algorithms with closed-loop property and the robust iterative learning control algorithms. The design problems for non-fragile controller and robust filter have been also considered. The results obtained have been applied to two systems with repetitive task, i.e., robot manipulator and uninterrupted power system (UPS). The simulations show that the control system using these new iterative learning control algorithms possess the rapid convergent speed and the stronger robustness. The good control behaviour has been also achieved.

本项目研究一类不确定线性系统和一类不确定非线性系统的鲁棒迭代学习控制问题，并研究无需复位初始跟踪误差的迭代学习控制算法。该算法既能保证系统具有鲁棒稳定，而且还具有鲁棒跟踪，且可以用于非重复工作即连续工作的控制系统，根据研究的结果，采用80C196单片机研制通用的先进控制器。因此这项研究不仅具有理论意义，而且还具有实用价值。