Stochastic linear quadratic optimal tracking control for discrete-time systems with delays based on Q-learning algorithm

Author:

Tan Xufeng1,Li Yuan1,Liu Yang2

Affiliation:

1. School of Science, Shenyang University of Technology, Shenyang 110870, China

2. School of Electrical and Electronic Engineering, Shenyang University of Technology, Shenyang 110870, China

Abstract

<abstract><p>In this paper, a reinforcement Q-learning method based on value iteration (Ⅵ) is proposed for a class of model-free stochastic linear quadratic (SLQ) optimal tracking problem with time delay. Compared with the traditional reinforcement learning method, Q-learning method avoids the need for accurate system model. Firstly, the delay operator is introduced to construct a novel augmented system composed of the original system and the command generator. Secondly, the SLQ optimal tracking problem is transformed into a deterministic one by system transformation and the corresponding Q function of SLQ optimal tracking control is derived. Based on this, Q-learning algorithm is proposed and its convergence is proved. Finally, a simulation example shows the effectiveness of the proposed algorithm.</p></abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

General Mathematics

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