A Recent Survey of Event Triggered Control of Nonlinear Systems

TL;DR

This survey reviews recent developments in event-triggered control systems for nonlinear systems, highlighting their applications, challenges like Zeno effect, and solutions involving neural networks and fuzzy logic.

Contribution

It provides a comprehensive overview of existing research, identifying challenges and evaluating the effectiveness of various solutions in event-triggered control of nonlinear systems.

Findings

Event-triggered control systems effectively manage errors and failures.

Neural networks and fuzzy logic enhance control system adaptability.

Challenges include non-linearity, Zeno effect, and multi-agent communication.

Abstract

The control systems are an essential part of every engineering system in any industrial application. The basic purpose of controls is to manage the internal operations of the system and detect any unwanted or uncertain situation. Failure in any system may be caused by various reasons and need to be restricted to save other components of the system. event-triggered control systems manage errors and failures by identifying particular events caused by failure and then mitigate the effect of that failure as much as possible. These systems have become prominent due to their smooth and autonomous way of detecting and minimizing risk in any system. The input to state stability property and a perfectly stabilized control are assumed for applying event-triggered control systems. Switching topologies can be utilized as an alternative to ISS assumptions to design triggering events. There are some issues related to this approach like non-linearity of systems, multi-agent communication, heterogeneity and Zeno effect in systems while monitoring, controlling faults and taking feedbacks. The efficiency of neural networks and fuzzy technologies are considered in this regard which are investigated by many researchers in the last decade. This survey reviews all work related to event-triggered control systems, their applications, challenges and possible solutions. The adaptability of these controls is evaluated based on the solutions available as well as the applicability of solutions proposed by researchers.