Learning Control Supported by Dynamic Event Communication Applying to Industrial Systems
Chaoxu Mu, Ke Wang, Changyin Sun
Abstract
For the practical control system, the controller is normally implemented on a digital platform with a time-triggered scheme. This scheme maybe produces redundant control and resources wasting, and hence, an event-triggered scheme is gradually favored. In this article, the robust learning control scheme is proposed aiming at a class of disturbed control systems, in which the system information is processed by a novel dynamic event communication. First, the robust optimal control problem with external disturbances is redescribed as a zero-sum differential game, and with integral reinforcement learning, a model-independent weight tuning law is devised for a critic neural network. Then, in order to further reduce the computational burden, an additional dynamic variable is put forward to incorporate the past triggering information. The application of a single-link joint arm system demonstrates that the proposed scheme can guarantee learning performance and robust control effect, along with larger triggering intervals. Finally, the load frequency control problem of single-area power system is studied. On one hand, the comparative results of five control schemes reveal that the dynamic event scheme can achieve the better frequency response at the lowest information transmission rate. On the other hand, the advantages of the proposed method are illustrated by comparing with other three event-triggered schemes.