Abstract
In this article, a biologically motivated two-level event-triggered mechanism is proposed to design a neuroadaptive controller with exponential convergence property. Specifically, an exponential adaptive neural network controller is designed, and a two-level event-triggered mechanism is developed for a class of nonlinear systems. The two-level event-triggered mechanism, which incorporates both static and dynamic event-triggered features, is motivated by the biological response to low- and high-speed changes in the environment. We also introduce a method in which time-varying control gain is used to achieve exponential convergence of the plant state. The effectiveness of the proposed control scheme is validated by numerical simulations. The minimal interevent time internal is lower bounded by a positive number, so no Zeno behavior occurs.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE transactions on neural networks and learning systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
