Abstract
This article investigates the finite-time adaptive neural network event-triggered output feedback control for the permanent magnet synchronous motor (PMSM) systems. The addressed PMSM systems include unknown nonlinear dynamics and immeasurable states. The neural networks are utilized to approximate the unknown nonlinear dynamics and an equivalent control design model is established, by which a neural network state observer is given to estimate the immeasurable states. By constructing an event-triggered mechanism and under the framework of adaptive backstepping control design technique and finite-time stability theory, a finite-time adaptive event-triggered output feedback control scheme is developed. It is proved that the proposed control scheme ensures the closed-loop system to be stable and the angular velocity, stator current and other state variables remain bounded in a finite time. Finally, the computer simulation is provided to confirm the effectiveness of the presented controllers.
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 callDisclaimer: 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.
