Neural-network-based model predictive control for consensus of nonlinear systems

This paper addresses the consensus problem for discrete-time nonlinear multi-agent systems subjected to switching communication topologies with a model predictive control (MPC) approach. For systems following a Markovian switching law, it is difficult for the existing MPC solutions to obtain reliable optimization based on the model predictions. We propose a new neural-network-based algorithm that reduces the effects of communication deficiencies by approximating and minimizing the MPC’s cost function in real-time. The convenience of the proposed method is certified in simulations for different applications and scenarios.