Networked Learning Predictive Control of Nonlinear Cyber-Physical Systems

Abstract

Cyber-physical systems integrate computing, network and physical environments to make the systems more efficient and cooperative, and have important and extensive application prospects, such as the Internet of things. This paper studies the control problem of nonlinear cyber-physical systems with unknown dynamics and communication delays. A networked learning predictive control scheme is proposed for unknown nonlinear cyber-physical systems. This scheme recursively learns unknown system dynamics, actively compensates for communication delays and accurately tracks a desired reference. Learning multi-step predictors are presented to predict various step ahead outputs of the unknown nonlinear cyber-physical systems. The optimal design of controllers minimises a performance cost function which measures the tracking error predictions and control input increment predictions. The system analysis leads to the stability criteria of closed-loop nonlinear cyber-physical systems employing the networked learning predictive control scheme. An example illustrates the outcomes of the proposed scheme.