Estimation of Road Profile for Suspension Systems Using Adaptive Super-Twisting Observer

The performance evaluation of active suspension system of vehicle is critically reliant on estimation of random road roughness profile. In this paper, the nonlinear dynamics of spring and damper of the active suspension system are considered to develop a nonlinear model excited by the random road excitation profile as an unknown input. To estimate the unknown input, an adaptive super-twisting algorithm based observer is designed for estimation of the road profile and states of the system. Under Lipschitz conditions, the convergence of the error dynamics is then proven. The effectiveness of the proposed observer for state and unknown input estimation is shown through simulation results performed for the Ford Fiesta MK2 vehicle suspension dynamics.