Coordinated Control of Networked Nonlinear Multiagent Systems Using Variable Horizon Learning Predictors via Cloud Edge Computing

Abstract

Networked multiagent systems use network technology to realize the interconnection, intercommunication, and mutual control of things. The coordinated control problem of networked nonlinear multiagent systems via cloud edge computing is investigated in this article. A mist–fog–cloud predictive control scheme is proposed for the coordinated control of complex large-scale networked multiagent systems by making use of the advantages of cloud edge computing. This scheme actively compensates for communication delays and achieves desired coordination performance of individual agents. Variable horizon learning predictors are presented to predict the outputs of the unknown nonlinear dynamical agents within different horizons. The design of coordinated control optimizes a performance index function presented to measure the coordination between agents. The analysis on a networked nonlinear multiagent system using the mist–fog–cloud predictive control scheme results in the conditions of simultaneous consensus and stability of the entire closed-loop system. An example demonstrates the effectiveness of the proposed scheme.