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

• Control of discrete-time nonlinear systems with uncertainties and disturbances.
• Industrial application.
• Theoretical and experimental contribution.
• Stability proof on the basis of Lyapunov theory.

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®MATLAB®,1 a dSPACE®dSPACE®2 DS1104 controller board and its software RTI libraries and ControlDesk®ControlDesk®,3 respectively, to control a Linear Induction Motor Lab-Volt®Lab-Volt®4 8228.