Consensus-Based Value Iteration for Multiagent Cooperative Control

In this paper, we consider the cooperative control problem for a class of discrete-time nonlinear multiagent systems with the objective of minimizing a group cost functional. A multiagent Hamilton-Jacobi-Bellman (HJB) equation is first derived and then a new consensus-based value iteration algorithm is proposed to seek the online approximate solution to multiagent HJB. Neural networks are employed to parameterize the state value functions for individual agents, and a novel adaptive law for updating neural network weights is proposed based on the estimation of several global terms. The proposed local information based cooperative control is based on the minimization of the overall cost functional which is the sum of all individual agents’ cost functionals. Numerical simulations show the effectiveness of the proposed design.