Data-Driven Adaptive Control for Discrete-Time Linear Systems With Delayed Inputs
针对输入时滞且系统动态未知的离散线性系统，提出一种基于值迭代的自适应动态规划算法，利用系统轨迹数据学习状态反馈控制器，无需初始稳定控制器，并通过两个实例验证有效性。
In this article, the stabilization problem is investigated for input-delayed systems with unknown system dynamics. To solve this problem, a value iteration (VI)-based adaptive dynamic programming (ADP) algorithm is established to learn the state feedback controller from the data along the trajectory of the system. In order to design this control algorithm, the input-delayed system is transformed into a delay-free system at first. Thus, the algebraic Riccati matrix equation (ARE) of the delay-free system is iteratively solved in the absence of system model, and then the controller is designed by using the approximation to the solution of the ARE. In particular, the rank condition of the data-constructed matrices is satisfied by utilizing basis functions, and an initial stabilizing controller is not required in the proposed algorithm. Finally, the effectiveness of the proposed algorithm is illustrated by two practical examples.