Suboptimal learning control for nonlinear dynamic systems

Adaptive iterative learning control is a popular approach to control parametric time-varying systems over finite time interval. However, initial resetting condition limits the application of a large amount of learning control algorithms. In this paper, we propose a suboptimal iterative learning control for a class of dynamic parametric systems to solve this problem. An error-tracking control strategy is used to deal with initial position problem, with suboptimal strategy used to accelerate convergence speed. As the iteration increases, the tracking error of the closed-loop system accurately follows the desired error trajectory over the entire time interval, and the system state tracks the reference signal perfectly on the pre-specified interval. The performance of the proposed suboptimal error-tracking learning control scheme is illustrated by an example involving both suboptimal errortracking algorithm and normal error-tracking algorithm.