Author : Shital M. Pawar and A.A. Panchwadkar

Suspension systems are an integral part of the new age vehicles. These suspension systems contribute in supporting vehicle weight, and also increase vehicle stability and cut off the driver from road roughness. Active suspension systems use an actuator which is governed by the control strategy using an ECU. Most of the literature focuses on feedback control of active suspension systems. This study aims to make use of an algorithm which predicts the states of the suspension systems in response to the road input using Kalman filter and control the suspension travel between the sprung and unsprung masses of the suspension. The Kalman filter is used as the observer which will observe the system states and predict the next states of the plant model. A linear Quadratic Regulator (LQR) and a Linear Quadratic Gaussian (LQG) control strategy is used to control the required force to minimize the suspension travel. The suspension system model is prepared in Matlab™/Simulink and simulated. Comparison of the estimation errors in open loop (passive suspension), the LQR control and LQG control using Kalman filter is made to study the effectiveness of the new control strategy.

Keywords: Active suspension system, suspension travel, Kalman filter, Linear Quadratic Regulator, Linear Quadratic Gaussian, estimation errors, control strategy.

Article published in International Journal of Current Engineering and Technology, Vol.7, No.2 (April-2017)