Distributed Model Predictive Control for Linear Multiagent System with Unknown Parameters

This paper investigates the distributed consensus control problem for linear multiagent systems with unknown system parameters. First, a time-varying estimation system is designed to dynamically estimate the unknown parameters. At each sampling instant, an iterative estimation algorithm is developed to update the parameter estimates using state errors and control inputs. Based on the estimation system, a distributed model predictive control (MPC) algorithm is proposed to achieve consensus. The Lyapunov stability theory is employed to rigorously prove consensus convergence for both the estimation system and the original multiagent system. Finally, the complete distributed MPC algorithm is presented and its effectiveness is demonstrated through a numerical simulation.