Title: Neuronal active anti-roll control of a single unit heavy vehicle associated with RST control of the hydraulic actuator
Authors: Saad Babesse; Djemeleddine Ameddah
Addresses: Department of Electrical Engineering, Institute of Technology, Ibn Khaldoune University, Tiaret, 14000, Algeria ' Department of Electrical Engineering, El-Hadj Lakhdar University, Batna, 05000, Algeria
Abstract: In this paper, a learning algorithm using neuronal networks to improve the roll stability and prevent the rollover in a single unit heavy vehicle is proposed. First, a linear quadratic regulator (LQR) to keep balanced normalised rollovers, between front and rear axles, below the unity, is designed; the data collected from this controller is used to train the artificial neuronal network (ANN), whose inputs are quite selected. The neuronal controller is thereafter applied for linear side force model, with constant and variable friction, which gives satisfactory results. And since the hydraulic actuator has eight order transfer function with uncontrollable states, an effective method to calculate the reduced order model (ROM) of the original high order model (HOM) is elaborated. The actuator has been approximated by a fully controllable second order model, which is suitable for feedback controllers. The control of the hydraulic actuator is based on a polynomial approach controller (R, S, T). This last RST controller is applied to control the roll angle of the actuator and simulations are carried out to show the effectiveness of the procedure.
Keywords: friction coefficient; hydraulic actuators; artificial neural networks; ANNs; reduced order model; vehicle rollover; RST control; single unit heavy vehicles; anti-roll control; linear quadratic regulator; LQR; feedback control; roll angle; simulation.
International Journal of Heavy Vehicle Systems, 2015 Vol.22 No.3, pp.236 - 254
Received: 31 Oct 2014
Accepted: 24 Mar 2015
Published online: 14 Sep 2015 *