An optimal terminal iterative learning control approach for nonlinear discrete-time systems
非线性离散时间系统的最优终端迭代学习控制

This paper presents an optimal terminal iterative learning control (TILC) approach by considering only the terminal output tracking error instead of the whole output trajectory tracking error. The control signal is directly updated from the error information of the given final terminal point. The key contributions of the presented optimal terminal iterative learning control (ILC) is that the controller design and analysis only uses the measured I/O data without any modeling information of the plant and the monotonic convergence is guaranteed. In this sense, the proposed controller is a datadriven approach. Both rigorous mathematical analysis and simulation results illustrate the applicability and effectiveness of the proposed approach.