Distributed Nash equilibrium seeking based on adaptive optimisation in switching topology

In this paper, we consider the Nash equilibrium seeking approach of nonlinear multi-agent systems with unknown time-varying disturbances unmodelled dynamics and unmodelled dynamics. The propagation of local disturbance in the network and the lack of global information make it difficult for local players to make optimal decisions. To tackle this problem, a hierarchical control algorithm is designed for Nash equilibrium seeking. Disturbances and unmodelled dynamics are compensated by neural networks estimation in control signals to reduce their influence on group decision making. Combined with the gradient optimisation method, an adaptive distributed Nash equilibrium seeking method is implemented, which adapts to switching topology. Eventually, the efficiency of the algorithm is corroborated by the simulation of the motor-driven load.