Predictive Control of Networked Nonlinear Multiagent Systems With Communication Constraints

Abstract

This paper investigates the control problem of networked nonlinear multiagent systems with communication constraints and unknown dynamics, based on system input–output data. A cost function for the design of networked nonlinear multiagent control systems is presented, which considers not only the coordination performance between agents but also the performance of individual agents. Utilizing a data model to represent discrete-time nonlinear multiagent systems, a networked data-driven predictive control scheme is proposed to optimize control performance, compensate for communication constrains, and achieve both consensus and stability of networked nonlinear multiagent systems. An optimal control protocol is derived, which minimizes the cost function and makes compensation for communication constraints. The criteria of achieving both consensus and stability of networked nonlinear multiagent systems are provided. An example illustrates the performance of the proposed networked data-driven predictive control scheme.