Real-Time Implementation of a Discrete Reduced Order Neural Observer: Linear Induction Motors Application. Issue 3 (2nd July 2016)

• Record Type: Journal Article
• Authors: Alanis, Alma Y.
• Is Part Of: Intelligent automation & soft computing. Volume 22:Issue 3(2016)
• Page Start: 491
• Page End: 498
• Publication Date: 2016-07-02
• Subjects: Reduced order neural observers -- linear induction motor -- Recurrent high order neural networks -- Kalman filtering learning -- discrete-time nonlinear systems; Artificial intelligence -- Periodicals; Intelligent control systems -- Periodicals; 003.5
• DOI: 10.1080/10798587.2015.1097014
• Languages: English
• ISSNs: 1079-8587

Abstract

Abstract: This paper deals with a discrete-time neural observer for flux estimation of a linear induction motor, which is based on a Recurrent High Order Neural Network (RHONN). The RHONN weights are tuned on-line, with no off-line learning phase required, using an extended Kalman filter based algorithm. The observer stability and boundedness of the state estimation and NN weights are proven using the Lyapunov approach. Knowledge of the system model is not required. The applicability of this observer is illustrated by real-time implementation for flux estimation of a linear induction motor benchmark.