Model-Based adaptive event-Triggered control of nonlinear continuous-Time systems

This paper presents a model-based adaptive event-triggered control scheme for a class of uncertain single-input and single-output nonlinear continuous-time systems. To this aim, the explicit design of an associated controller is proposed by constructing an adaptive model, exploiting the principle of feedback linearization and using neural networks to approximate an unknown smooth nonlinear function. Then, the stability of the resulting impulsive dynamic system is strictly proved by using the Lyapunov stability theory, and the event-trigger condition is designed to realize that the NN weights and feedback signals are updated only when the condition is violated. In addition, the lower bound of inter event times is strictly proved to be positive, which effectively avoids Zeno behavior. Compared with the conventional adaptive control based on the time-triggered scheme, feedback transmissions and NN weight updating only occur at necessary instants, such that the waste of communication resources is effectively reduced. Finally, the effectiveness of the developed control algorithm is verified by a simulation example. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper presents a model-based adaptive event-triggered control scheme for uncertain single-input and single-output systems by constructing an adaptive model and using neural networks to approximate nonlinear functions. The stability of the resulting impulsive dynamic system is proven using Lyapunov theory, and the event-trigger condition reduces waste of communication resources by updating NN weights and feedback signals only when necessary.