Approximately adaptive neural cooperative control for nonlinear multiagent systems with performance guarantee

ABSTRACT

This paper studies the cooperative control problem for a class of multiagent dynamical systems with partially unknown nonlinear system dynamics. In particular, the control objective is to solve the state consensus problem for multiagent systems based on the minimisation of certain cost functions for individual agents. Under the assumption that there exist admissible cooperative controls for such class of multiagent systems, the formulated problem is solved through finding the optimal cooperative control using the approximate dynamic programming and reinforcement learning approach. With the aid of neural network parameterisation and online adaptive learning, our method renders a practically implementable approximately adaptive neural cooperative control for multiagent systems. Specifically, based on the Bellman's principle of optimality, the Hamilton–Jacobi–Bellman (HJB) equation for multiagent systems is first derived. We then propose an approximately adaptive policy iteration algorithm for multiagent cooperative control based on neural network approximation of the value functions. The convergence of the proposed algorithm is rigorously proved using the contraction mapping method. The simulation results are included to validate the effectiveness of the proposed algorithm.

KEYWORDS:

• Multiagent systems
• neural networks
• cooperative control
• multiagent policy iteration

Acknowledgments

The research leading to these results received funding from the Air Force Research Laboratory under grant agreements FA8750-13-1-0190 and FA8750-15-1-0143.

Disclosure statement

No potential conflict of interest was reported by the authors..

Additional information

Funding

This work was supported by the Air Force Research Laboratory [grant number FA8750-13-1-0109], [grant number FA8750-15-1-0143].