Distributed Model Predictive Control for Optimal Consensus of Constrained Multi-Agent Systems

This paper develops a framework based on distributed model predictive control to solve the optimal consensus problem for constrained multiagent systems. Taking both transient performance and final consensus state into consideration, the optimization problem in each prediction horizon is formed as a coupled optimization problem containing a nonseparable cost function with constraints. An efficient distributed algorithm is proposed with the distributed convergence conditions on the auxiliary parameters, which makes each agent solve its subproblems in parallel iterations, enhancing the running efficiency of the algorithm. The stability of the closed-loop system is analyzed, providing distributed stability conditions that only depend on the local information of each agent. Numerical simulations verify the validity of theoretical results by utilizing the proposed approach to a multi-robot system.