Model predictive consensus in networked autonomous systems using discrete Laguerre functions and event triggering approach

摘要

This paper presents the consensus problem of general linear discrete time networked autonomous agents, using distributed model predictive control (DMPC) and event triggering algorithm. The computational complexity of conventional DMPC is high due to the large values of prediction horizon and control horizon required for real time systems. The increased computational load becomes an obstruction in the practical implementation of DMPC. To solve the issue, a novel design methodology of parameterizing the control input using discrete Laguerre functions is adopted in this paper. The predictive control protocol is applied to a group of networked autonomous agents operating on event triggered consensus algorithm. The proposed event triggered algorithm offers the advantages of lesser communication between the agents along with reduction in controller updates. The closed loop stability of the networked system is established using Lyapunov criterion, based on the terminal state's equality constraint. Comparison of the proposed algorithm with existing works through numerical examples shows significant reduction in computational load, design parameters and data transmission, while achieving comparable performance. To validate the theoretical results, simulation is carried out on a group of autonomous vehicles and the usefulness of the proposed algorithm on system performance is illustrated. (c) 2022 European Control Association. Published by Elsevier Ltd. All rights reserved.