Title: Adaptive nonlinear observer augmented by radial basis neural network for a nonlinear sensorless control of an induction machine
Authors: Mourad Boufadene; Mohammed Belkheiri; Abdelhamid Rabhi
Addresses: Laboratory of Telecommunications, Signals and Systems, University Amar Telidji of Laghouat, PB. 37G Ghradaia Road, 03000, Laghouat, Algeria ' Laboratory of Telecommunications, Signals and Systems, University Amar Telidji of Laghouat, PB. 37G Ghradaia Road, 03000, Laghouat, Algeria ' Laboratory of Modeling, Information and Systems, University of Picardie Jules Verne, 33 rue Saint Leu – 80039 Amiens Cedex 1, France
Abstract: This paper presents adaptive neural network nonlinear observer associated with a sensor less nonlinear feedback linearisation controller for induction machine. The proposed observer is used to estimate the mechanical speed using the stator currents measurements and the supplied input voltages; whereas the load torque (unknown disturbance) is estimated using online radial basis neural network function approximation. The stability of the proposed controller-observer is achieved using Lyapunov function. Hence, simulation results have been performed under MATLAB/Simulink shows clearly the performance of the proposed algorithm.
Keywords: sensorless control; neural network observer; adaptive observer; load torque estimation; induction machine; feedback linearisation control; mechanical speed estimation; radial basis function approximation; field oriented control; FOC.
International Journal of Automation and Control, 2018 Vol.12 No.1, pp.27 - 43
Received: 05 Jul 2016
Accepted: 14 Oct 2016
Published online: 30 Oct 2017 *