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

Highlights：

• In this paper, the control inputs between two adjacent tasks change along with the dynamics of the adaptive model. Thus, the number of transmissions will be reduced compared with the traditional ETC scheme which maintain constant control input by using the ZOH, such that communication consumption can be future reduce.

• Unlike the traditional threshold with constants or system state vector alone, in this paper, an adaptive threshold that includes both the system state vectors and the NN weight estimates is designed.

摘要

•Compared with the conventional adaptive control based on time-triggered scheme, the proposed method realizes that the feedback signals and the NN weights are updated only when the event is triggered, which effectively reduces the consumption of communication.•In this paper, the control inputs between two adjacent tasks change along with the dynamics of the adaptive model. Thus, the number of transmissions will be reduced compared with the traditional ETC scheme which maintain constant control input by using the ZOH, such that communication consumption can be future reduce.•Unlike the traditional threshold with constants or system state vector alone, in this paper, an adaptive threshold that includes both the system state vectors and the NN weight estimates is designed.

论文评审过程：Received 3 November 2020, Revised 8 March 2021, Accepted 22 April 2021, Available online 25 May 2021, Version of Record 25 May 2021.