Professor of Mechanical Engineering and Smart Structures, School of Computing Engineering and Mathematics, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.
Abstract—Suspension in a vehicle is provided primarily to improve the passenger comfort and road handling in different road conditions. Active suspension is proven to be better than a passive suspension system. In this paper, a quarter car model is considered to study the performance of the proposed controller. Choosing the proper database to train an adaptive neural fuzzy inference(ANFIS) plays an important role in improving the suspension system performance. The database used to train the proposed ANFIS controller was extracted from a linear quadratic regulator (LQR) controller. The purpose of this paper is to investigate the performance of an active suspension system using ANFIS and LQR controllers. MATLAB/SIMULINK was used to study the simulation of vehicle’s performance on a road. The results show that both LQR and ANFIS controllers can effectively control the vertical vibration of the vehicle as compared to passive suspension system. Moreover the ANFIS control method is found to be more effective in reducing the acceleration of a sprung mass as compared to LQR control.
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