Neural-network-based model predictive control for consensus of nonlinear systems
Consensus control. Neural networks. Model predictive control. Multi-agent systems.
This work addresses, through a model predictive control (MPC) approach, the consensus problem for discrete-time nonlinear multi-agent systems subjected to switching communication topologies. For systems following a random switching law, there is not any MPC solution that results in a reliable optimization in real-time. We propose a new neural-network-based algorithm that reduces the effects of communication deficiencies, caused by Markovian switching, by approximating and minimizing, in real-time, the MPC’s cost function. The convenience of the proposed method is certified in simulations for different applications and scenarios. Finally, the future steps of the current research are detailed.