Coordination of low-power nonlinear multi-agent systems using cloud computing and a data-driven hybrid predictive control method

Abstract

This paper studies the coordinated control issue for low-power nonlinear multi-agent systems (LNMASs). To improve the calculation ability of the LNMAS for massive real-time data and alleviate the communication burden of individual agents, cloud computing systems are employed to establish the multi-agent system instead of low-power microprocessors. To reduce the adverse effects caused by the network delays and data loss in all communication channels and coordinate the outputs of all agents, a novel data-driven networked hybrid predictive control (DDNHPC) method using only input and output information of the system is proposed. In the DDNHPC method, the nonlinear agents are described as dynamic data models for controller design. Based on the dynamic data models, the hybrid predictive method including direct output predictions and further switching output predictions is designed to establish the active outputs coordination relationship among all agents. Simulation and cloud-based experimental examples are performed to illustrate the effectiveness and applicability of the proposed method. The achievement also provides a solution to implement the coordination of large-scale intelligent manufacturing.