RHONN identifier-control scheme for nonlinear discrete-time systems with unknown time-delays. Issue 1 (January 2018)

• Record Type: Journal Article
• Authors: Rios, Jorge D.; Alanis, Alma Y.; Lopez-Franco, Carlos; Arana-Daniel, Nancy
• Is Part Of: Journal of the Franklin Institute. Volume 355:Issue 1(2018)
• Page Start: 218
• Page End: 249
• Publication Date: 2018-01
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2017.11.014
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: This work presents a neural identifier-control scheme for uncertain nonlinear discrete-time systems with unknown time-delays. This scheme is based on a neural identifier to get a model of the system and a discrete-time block control technique based on sliding modes to generate the control law. The neural identifier is based on a Recurrent High Order Neural Network (RHONN) trained with an Extended Kalman Filter (EKF) based algorithm. Applicability is shown using real-time test results for linear induction motors. Also, a Lyapunov analysis is added in order to prove the semi-globally uniformly ultimately boundedness (SGUUB) of the proposed neural identifier-control scheme.