Title: Neural network augmented backstepping control for uncertain nonlinear systems - application to laboratory antilock braking system

Authors: Abdelhamid Rabhi; Mohammed Belkheiri; Jérôme Bosche; Ahmed El Hajjaji

Addresses: Laboratoire de Modélisation, Information et Systèmes, 33 Rue de Saint Leu, Amiens 80000, France ' Laboratoire de Télécommunications, Signaux et Systèmes, Université Amar Telidji de Laghouat, BP G37, Route de Ghardaia Laghouat 03000, Algeria ' Laboratoire de Modélisation, Information et Systèmes, 33 Rue de Saint Leu, Amiens 80000, France ' Laboratoire de Modélisation, Information et Systèmes, 33 Rue de Saint Leu, Amiens 80000, France

Abstract: A new control approach is proposed to address the tracking problem of a class of uncertain nonlinear systems. In this approach, one relies first on a partially known model of the system to be controlled using a backstepping control strategy. The obtained controller is then augmented by an online artificial neural network (ANN) that serves as an approximator for the neglected dynamics and modelling errors. Thus, the developed method combines backstepping approach and ANN to address the tracking problem for uncertain systems. The proposed approach is systematic, and exploits the known nonlinear dynamics to derive the stepwise virtual stabilising control laws. At the final step, an augmented Lyapunov function is introduced to derive the adaptation laws of the network weights. The suggested control algorithm is tested experimentally on a Laboratory ABS system showing satisfactory results although the system is highly nonlinear and with unknown physical parameters.

Keywords: neural network; ABS; backstepping control; Lyapunov; uncertain nonlinear systems.

DOI: 10.1504/IJANS.2016.085805

International Journal of Applied Nonlinear Science, 2016 Vol.2 No.4, pp.270 - 289

Accepted: 29 Aug 2016
Published online: 14 Aug 2017 *

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