Scalable and Constrained Consensus in Multiagent Systems: Distributed Model Predictive Control-Based Approaches

This article explores the challenge of achieving scalable and constrained consensus in general linear multiagent systems (MASs), where agents can occasionally join and leave the network. Two distributed model predictive control (DMPC)-based consensus methods are developed to tackle the scalability, performance, and constraint challenges. The first approach uses an innovative online DMPC optimization that integrates with a predesigned scalable consensus protocol, ensuring constraint satisfaction while achieving scalable consensus. The second method leverages tracking DMPC, enabling each agent to adhere to a locally evolving time-specific reference, which is continually updated through the utilization of the predicted state sequences from neighboring agents. Moreover, it is shown that the feasibility of the associated optimization problems can be recursively ensured with the suitably designed cost function and constraints. In addition, the scalable consensus property of the constrained MAS is guaranteed. Finally, the simulation results illustrate the effectiveness of the proposed algorithms.