Simultaneous Output Selection and Observer Design for Vehicle Suspension System With Unknown Road Profile

In this paper, a reduced-order observer is designed using an unknown input decoupling approach to estimate the road profile and the state variables of vehicle suspension system. During the observer design process, a new concept called the degree of observability (DO) is introduced to quantitatively describe the strengths and weaknesses of the system observability in terms of different outputs. This concept integrated with the decoupling conditions lead to a systematic method for selecting the suitable outputs of the suspension system for the observer design. In this method, among all output combinations that satisfy the decoupling requirements, the combination of sprung mass displacement and unsprung mass velocity is found to have the highest DO containing the most information of the system. The performance of the designed observer with the selected outputs is compared with the Kalman filter (KF) developed in the literature by assuming the initial random walk model for the road profile. To have a nearly real situation for simulation studies, a MacPherson suspension model constructed in the ADAMS multibody software is employed. The results obtained by deterministic and stochastic road excitations indicate much more accurate estimations for the designed observer with minimum outputs compared with the KF. The estimation errors are quantitatively compared with the KF using different set of outputs to confirm the positive effect of simultaneous DO analysis and decoupling approach. Meanwhile, the proposed observer is free of assumptions, and its structure is more simple and easier for implementation.