Distributed Model Predictive Control Framework for Consensus of Multi-Agent System

Considering the widespread applications of the model predictive control (MPC), such as industrial process control and robotic operations, this paper proposes an MPC-based asynchronous distributed algorithm to solve the consensus problem of multi-agent system (MAS) by using the operator splitting method. The proposed algorithm is asynchronous, which means that all agents do not require to update and communicate simultaneously. When the positive uncoordinated step sizes satisfy the given condition, the optimal solution can be worked out by using the proposed algorithm. Moreover, all agents are allowed to set the step sizes locally based on local information. Numerical simulation validates the feasibility and effectiveness of the proposed algorithm.