DSC-based Parallel Control for Consensus Maneuvering of Multi-agent Systems subject to Unmatched Uncertainties based on Distributed Nash Equilibrium Seeking

We consider parameterized virtual leader-guided distributed consensus maneuvering of uncertain multi-agent systems herein. The model of the agent is in the strict-feedback form and subject to unmatched uncertainties. In comparison with existing results, we try to research distributed consensus maneuvering from a distributed game perspective. Inspired by distributed Nash equilibrium seeking approach and cooperative learning strategy, a parallel control method is developed for consensus maneuvering, including cooperative learning-based neural predictors, dynamic surface control(DSC)-based parallel control law, and parameter update law for virtual leader. It is proved that the total closed-loop system is input-to-state stable, and the outputs of all agents converge to the Nash equilibrium as far as possible. The main results are demonstrated by theoretical analysis and a simulation example.