Real-time discrete neural control applied to a Linear Induction Motor

• Published in: Neurocomputing (Amsterdam) Vol. 164; pp. 240 - 251
• Main Authors: Alanis, Alma Y., Rios, Jorge D., Rivera, Jorge, Arana-Daniel, Nancy, Lopez-Franco, Carlos
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 21.09.2015
• Subjects: Discrete-time sliding modes; Extended Kalman Filter; Linear Induction Motor; Lyapunov; Recurrent High Order Neural Network; Extended Kalman Filter; Recurrent High Order Neural Network; Linear Induction Motor; Lyapunov; Discrete-time sliding modes
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2015.02.065

This work presents a real-time discrete nonlinear neural identifier based on a Recurrent High Order Neural Network (RHONN) trained online with an Extended Kalman Filter (EKF) based algorithm applied to a Linear Induction Motor (LIM). For the obtained neural model, a discrete-time sliding mode control law is designed for trajectory tracking of velocity and flux magnitude. The stability analysis is also included, based on the Lyapunov approach. This work is implemented in real-time by using MATLAB®,11MATLAB is a registered trademark of The MathWorks, Inc. a dSPACE®22dSPACE is a registered trademark of DSPACE GmbH. DS1104 controller board and its software RTI libraries and ControlDesk®,33ControlDesk is a registered trademark of DSPACE GmbH. respectively, to control a Linear Induction Motor Lab-Volt®44LAB-Volt is a registered trademark of Lab-Volt Systems, Inc. 8228. •Control of discrete-time nonlinear systems with uncertainties and disturbances.•Industrial application.•Theoretical and experimental contribution.•Stability proof on the basis of Lyapunov theory.