Integral reinforcement learning based decentralized optimal tracking control of unknown nonlinear large-scale interconnected systems with constrained-input

摘要

This paper deals with the decentralized optimal tracking control problem of large-scale interconnected systems with constrained-input. The large-scale interconnected systems are firstly transformed to several nominal isolated subsystems. Then, nominal isolated subsystems tracking problem is solved via integral reinforcement learning (IRL) method. It is proved that the solved optimal controllers ensure the bound-edness of the original systems tracking error. The actor-critic neural network (NN) technique is used to approximate the critic cost and control policy to implement the IRL algorithm. The least squares approach is employed to solve the weights of actor-critic NN by using only system data. A simulation example is provided to verify the effectiveness of the controllers by comparing with the controllers without considering constrained-input. (C) 2018 Elsevier B.V. All rights reserved.