Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

Abstract

Network Control Systems (NCSs) have attracted much interest over the past decade as part of a move towards more decentralised control applications and the rise of cyberphysical system applications. Many practical NCSs face the challenges of limited communication bandwidth resources, reliability and lack of knowledge of network dynamics, particularly when wireless networks are involved. Machine learning (ML) combined with event-triggered control (ETC) has the potential to ease some of these challenges. For example, ML can be used to overcome the problem of a lack of network models by learning system behaviour or adapt to dynamically changing models by continually learning model dynamics. ETC can help to conserve bandwidth resources by communicating only when needed or when resources are available. Here, we present a review of the literature on work that combines ML and ETC. The literature on supervised, semi-supervised, unsupervised and reinforcement learning based approaches such as deep reinforcement learning and statistical learning in combination with ETC is explored. Furthermore, the difference between the application of these learning algorithms on model-based and model-free systems are discussed. Following the analysis of the literature, we highlight open research questions and challenges related to ML-based ETC and propose approaches to possible solutions to these challenges.

Data-driven analysis and controller design for discrete-time systems under aperiodic sampling

“…Since the importance of data as well as of cyber-physical, embedded and networked control systems continues to grow, combining concepts from data-driven control and sampleddata control is a highly relevant research direction. The datadriven analysis of aperiodically sampled systems, as presented in this work, may contribute to this emerging field by providing a novel approach to model and analyze a great variety of problems at the intersection of data-driven and sampled-data control, such as learning event-triggered control [48], learning unknown channel conditions [49], or data-driven network access scheduling [50]. As in the continuous-time case, the crucial property of ∆ in the lifted domain is that it is static, i.e., the output e in each time interval N [t k ,t k+1 −1] depends on the input y in the same interval only.…”