Neural-network-based decentralized control of continuous-time nonlinear interconnected systems with unknown dynamics

• Published in: Neurocomputing (Amsterdam) Vol. 165; pp. 90 - 98
• Main Authors: Liu, Derong, Li, Chao, Li, Hongliang, Wang, Ding, Ma, Hongwen
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2015
• Subjects: Adaptive dynamic programming; Decentralized control; Neural networks; Optimal control; Policy iteration; Policy iteration; Adaptive dynamic programming; Decentralized control; Neural networks; Optimal control
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2014.07.082

In this paper, we establish a neural-network-based decentralized control law to stabilize a class of continuous-time nonlinear interconnected large-scale systems using an online model-free integral policy iteration (PI) algorithm. The model-free PI approach can solve the decentralized control problem for the interconnected system which has unknown dynamics. The stabilizing decentralized control law is derived based on the optimal control policies of the isolated subsystems. The online model-free integral PI algorithm is developed to solve the optimal control problems for the isolated subsystems with unknown system dynamics. We use the actor-critic technique based on the neural network and the least squares implementation method to obtain the optimal control policies. Two simulation examples are given to verify the applicability of the decentralized control law.