Title: Observer-based stabilisation of some non-linear non-minimum phase systems using neural network

Authors: S.M. Hoseini, M. Farrokhi

Addresses: Department of Electrical Engineering, Center of Excellence for Power System Automation and Operation, Iran University of Science and Technology, Narmak, Frajam St., Tehran 16846, Iran. ' Department of Electrical Engineering, Center of Excellence for Power System Automation and Operation, Iran University of Science and Technology, Narmak, Frajam St., Tehran 16846, Iran

Abstract: This paper presents a neuro-adaptive output-feedback stabilisation method for non-linear non-minimum phase systems with partially known Lipschitz continuous functions in their arguments. The proposed controller is comprised of a linear, a neuro-adaptive, and an adaptive robustifying control term. The adaptation laws for the neural network weights are obtained using the Lyapunov|s direct method. These adaptation laws employ a suitable output of a linear state observer that is realisable. The ultimate boundedness of the error signals will be shown through analytical work using Lyapunov|s method. The effectiveness of the proposed scheme will be shown in simulations for the benchmark single flexible link manipulator and translation oscillator rotational actuator (TORA) problems.

Keywords: neuro-adaptive control; nonlinear systems; non-minimum phase; output feedback; adaptive control; observer-based stabilisation; neural networks; robust control; simulation; single flexible link manipulators; translation oscillator rotational actuators; TORA.

DOI: 10.1504/IJMIC.2010.035275

International Journal of Modelling, Identification and Control, 2010 Vol.11 No.1/2, pp.15 - 25

Published online: 20 Sep 2010 *

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