A contemporary adaptive air suspension using LQR control for passenger vehicles

Long rides on irregular roads and infrastructure problems like uncomfortable seating have a very bad impact on human body. The passengers suffer not only physical pain but also stress related problems. Airsprings gain more popularity in passenger vehicles with an increase in demand for ride comfort. Ride comfort and vehicle handling, being the two critical factors of a suspension system often contradict each other. This led to an extensive research on active automobile suspension systems. The authors of this article propose an innovative design of adaptive air suspension system with LQR control strategy. The proposed LQR controller is tuned by Particle Swarm Optimization. A dynamic model of an air suspension system used in passenger vehicles was designed and simulated for both passive and adaptive systems in MATLAB. An experimental evaluation was done to check the performance of the adaptive air suspension system on a vibration shaker table. Air suspension is a non-linear system and thus the authors have derived a stiffness equation for the same with minimal assumptions. A comparative analysis between the most commonly used PID controller and proposed LQR controller was performed over bumps, potholes and ISO standard random roads in MATLAB. Simulation results showed that adaptive air suspension system improves the ride comfort by reducing the maximum displacement amplitude of the vehicle over random roads by 31% while ensuring the stability of the vehicle by reducing the settling time by 85%. The experimental results of an adaptive air suspension system subjected to random vibrations of frequencies between 5 Hz to 20 Hz, exhibited a reduction of sprung mass acceleration by about 30% demonstrating that the proposed controller is effective for random vibration inputs. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.