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Article Dans Une Revue International Journal of Modelling, Identification and Control Année : 2020

RBF NN observer based adaptive feedback control for the ABS system under parametric uncertainties and modelling errors

Résumé

An antilock braking (ABS) scheme control is a relatively difficult task due to its uncertain nonlinear dynamics. According to the requirement that the braking process must be fast and robust, we contribute to extending the universal function approximation property of the radial-basis-function (RBF) neural network (NN) to design both: (a) adaptive NN observer to estimate the tracking error dynamics; and (b) intelligent NN output feedback controller (OFC) that will overcome successfully the existing high uncertainties. Notice that the OFC is introduced to linearise the ABS nonlinear system, and the dynamic compensator is involved to stabilise the linearised system. The estimated states are used in the adaptation laws as an error signal. Simulations of the proposed control algorithm based adaptive RBFNN observer are conducted then compared to the Bang-bang controller to demonstrate its practical potential. Furthermore, its efficiency has been successfully confirmed through a robustness test.
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Dates et versions

hal-03199823 , version 1 (16-04-2021)

Identifiants

  • HAL Id : hal-03199823 , version 1

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Hamou Ait Abbas, Abdelhamid Rabhi, Mohammed Belkheiri. RBF NN observer based adaptive feedback control for the ABS system under parametric uncertainties and modelling errors. International Journal of Modelling, Identification and Control, 2020. ⟨hal-03199823⟩
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