Real‐time model‐free resilient control for discrete nonlinear systems. Issue 5 (28th April 2021)

• Record Type: Journal Article
• Authors: Alanis, Alma Y.; Alvarez, Jesus G.
• Is Part Of: Asian journal of control. Volume 23:Issue 5(2021)
• Page Start: 2097
• Page End: 2111
• Publication Date: 2021-04-28
• Subjects: discrete‐time nonlinear systems -- model‐free control -- neural control -- recurrent neural networks -- resilient control; Automatic control -- Periodicals; Control theory -- Periodicals; 629.805
• DOI: 10.1002/asjc.2564
• Languages: English
• ISSNs: 1561-8625

Abstract

Abstract: Motivated 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.