Fuzzy adaptive event-triggered output feedback control for nonlinear systems with tracking error constrained and unknown dead-zone

ABSTRACT

In this paper, an adaptive fuzzy event-triggered output feedback control scheme is studied for a class of non-strict feedback systems with tracking error constrained and unknown dead-zone. Fuzzy logical systems (FLSs) are employed to solve the uncertainties of the controlled plant, and the immeasurable states are estimated by employing the fuzzy observer. In the designed observer, to compensate for the effect of unknown dead-zone by invoking the known designed event-triggered input signal, an adaptive parameter is designed. Furthermore, due to event-triggered control (ETC) places a great burden on the computing space, the dynamic surface control (DSC) technique is used to solve the problem of ‘explosion of complexity’ in each step, and the simplified barrier Lyapunov function (SBLF) is utilised to restrict the tracking error within the prescribed boundaries. Consequently, the designed parameter adaptive laws and event-triggered controller can ensure that all signals of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Ultimately, two numerical simulation examples are provided to illustrate the effectiveness of the proposed event-triggered control theory.

Keywords:

• Adaptive fuzzy control
• backstepping
• dead-zone
• simplified barrier Lyapunov function
• event-triggered control

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by National Natural Science Foundation of China (NNSF) of China under Grant 61822307 and Grant 61773188.

Notes on contributors

Kunting Yu

Kunting Yu received the BS degree in information and computing science from the Liaoning University of Technology, Jinzhou, China, in 2018, where he is currently pursuing the ME degree in applied mathematics. His current research interests include sampled-data control, event-triggered control, fuzzy control, and adaptive control.

Yongming Li

Yongming Li received the BS degree and the MS degree in Applied Mathematics from Liaoning University of Technology, Jinzhou, China, in 2004 and 2007, respectively. He received the PhD degree in Transportation Information Engineering & Control from Dalian Maritime University, Dalian, China in 2014. He is currently a Professor in the College of Science, Liaoning University of Technology. His current research interests include adaptive control, fuzzy control and neural networks control for nonlinear systems.