Title: Improvement of LQR active anti-roll control of a single-unit heavy vehicle by means of a trained artificial neuronal network

Authors: Saad Babesse; Djemeleddine Ameddah

Addresses: Department of Electrical Engineering, Institute of Technology, Ibn Khaldoune University, Tiaret 14000, Algeria ' Department of Electrical Engineering, l-Hadj Lakhdar University, Batna 05000, Algeria

Abstract: In this paper, a neuronal network is used to improve a Linear-Quadratic Regulator (LQR) active anti-roll control applied to a single-unit heavy vehicle suspension with linear and non-linear side force model. First, to keep the normalised rollovers between front and rear axles, equal to or below unity, the LQR control is proposed. After that, the training data collected from this controller are used as a training basis of a neuronal regulator. The artificial neuronal network controller is thereafter applied for the non-linear side force model, and it gives more satisfactory results than the LQR.

Keywords: LQR control; linear quadratic regulator; vehicle rollover; single-unit heavy vehicles; adhesion coefficient; nonlinear side force; active control; anti-roll control; vehicle safety; artificial neural networks; ANNs; vehicle suspension; side force modelling.

DOI: 10.1504/IJVS.2016.079657

International Journal of Vehicle Safety, 2016 Vol.9 No.2, pp.166 - 179

Accepted: 09 Jun 2016
Published online: 04 Oct 2016 *

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