Abstract
AbstractMotivated by the increasing complexity of systems to be controlled and different system components that cause uncertainties, threats, and disturbances, among other system stressing phenomena. Resilient control is an important design paradigm that has attracted attention from academics, practitioners, and the industrial sector. Therefore, this paper proposes the design of a model‐free resilient control for unknown discrete‐time nonlinear systems based on a recurrent high order neural network trained with an on‐line extended Kalman filter‐based algorithm for output trajectory tracking in the presence of uncertainties, disturbances, and unmodeled dynamics. This paper also includes the stability proof of the entire proposed scheme; its applicability is shown via simulation and experimental results including a comparative analysis of the proposed controller against well‐known controllers for a three‐phase induction motor.
Sign-in/Register to access full text options 
Free) 
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 call
