Data-Driven Optimal Consensus Control for Switching Multiagent Systems via Joint Communication Graph

This article investigates optimal consensus problems of switching multiagent systems (MASs). For such kind of MASs, local neighborhood tracking error (LNTE) system is time varying because of the switching communication graph (CG). Existing performance index defined on the LNTE system is thus invalid for the switching MASs. This article addresses this problem by defining a new augmented LNTE system. The augmented LNTE system is constructed using the joint CG and is thus time-invariant. Subsequently, the optimal consensus problems for the MASs are formulated using the augmented LNTE system. Value iteration algorithm that employs an actor-critic network is used to learn the optimal controller. The article provides a theoretical analysis demonstrating the learning stability and control stability of the value iteration method. Furthermore, we also show that the MASs will reach approximate Nash equilibrium. Simulation results proves the effectiveness of the proposed method.