基于遗忘因子的数据驱动最优迭代学习控制算法研究.

A forgetting factor based data-driven optimal iterative learning control method is proposed for a class of nonlinear nonaffine discrete-time systems running repeatedly in finite time. First, an iterative dynamic linearization method is introduced to transform the nonlinear system into a linear input and output incremental form. Second, the main problems of the optimal iterative learning method are analyzed. To solve the problem that the control input cannot respond in time due to the accumulation effect of historical information, an adaptive forgetting factor is designed to make the method more controllable and flexible. The proposed control method is a data-driven control approach, and the design and analysis process only depends on the input and output data of the system and does not contain any explicit model information. Finally, the effectiveness of the proposed method is verified by simulation results. [ABSTRACT FROM AUTHOR]