Discrete-Time Self-Learning Parallel Control
Qinglai Wei, Lingxiao Wang, Jingwei Lu, Fei–Yue Wang
Abstract
In this article, a new self-learning parallel control method, which is based on adaptive dynamic programming (ADP) technique, is developed for solving the optimal control problem of discrete- time time-varying nonlinear systems. It aims to obtain an approximate optimal control law sequence and simultaneously guarantees the convergence of the value function. Establishing the time-varying artificial system by neural networks in a certain time-horizon, a control-sequence-improvement ADP algorithm is developed to obtain the control law sequence. For the first time, the criteria of the parallel execution are presented, such that the value function is proven to converge to a finite neighborhood of the optimal performance index function. Finally, numerical results and analysis are presented to demonstrate the effectiveness of the parallel control method.