Title: Optimisation of robust and LQR control parameters for discrete car model using genetic algorithm
Authors: Mohammed Kaleemullah; Waleed F. Faris
Addresses: Department of Electronics and Communication Engineering, C. Abdul Hakeem College of Engineering and Technology, Vellore – 632509, Tamil Nadu, India ' Faculty of Engineering, International Islamic University, Malaysia, Kuala Lumpur, Malaysia
Abstract: Active suspension systems are the main feature in modern cars and will be the main stream in the future. The optimisation of their performances requires many studies about the different types of controllers. Robust H-infinity and linear quadratic regulator (LQR) controllers are used to control the suspension system and to reduce the vibrations in the car and to improve handling. A half car discrete model is considered in this research to study the effects on passengers owing to different road profiles. The weights of robust H-infinity and LQR controllers are obtained using genetic algorithm on a half car model with two different types of common road disturbance. The design parameters of both the active controllers vary with various road profiles. This proves that particular design parameters in robust and LQR controller do not have the ability to adapt to the variations in road surface. Furthermore, active controllers significantly improve the performance of the system in all aspects when compared to passive systems.
Keywords: robust control; LQR control; genetic algorithm; vehicle vibration suppression; ride comfort; ride safety; half car model; optimising ride comfort; active suspension.
International Journal of Vehicle Systems Modelling and Testing, 2022 Vol.16 No.1, pp.40 - 63
Received: 17 Dec 2019
Accepted: 08 Feb 2020
Published online: 15 Nov 2022 *