Design and Experimental Validation of an Extended State Observer for Estimating of Uncertainties and Unknown Road Input in a Quarter car McPherson Suspension System

This paper presents the design and experimental implementation of the extended state observer (ESO) for a fabricated quarter-car suspension platform with a McPherson mechanism equipped with different sensors. This algorithm aims to estimate uncertainties and road input, and determines an accurate dynamic model for the vehicle suspension system. In the proposed method, the terms including uncertainties and unknown road input are added to the system as new state variables. Then, using data of sprung mass and unsprung mass displacements, these uncertainties and unknown inputs are estimated along with other state variables of the system. A nonlinear Kalman filter with unknown input is also designed to be compared with the ESO. The comparison results using the experimental data with measurement errors indicate the high accuracy of the ESO in constructing a precise dynamic model for the system. Meanwhile, the ESO uses fewer sensors and its regulation is easier. Both observers are used within the structure of active suspension system under an optimal nonlinear controller to provide the objectives of the suspension system. Co-simulation results of Adams/MATLAB show the better performance of the proposed controller using the ESO.