A study of a Kalman filter active vehicle suspension system using correlation of front and rear wheel road inputs

Abstract Based on a half-vehicle model, an algorithm is proposed for a Kalman filter optimal active vehicle suspension system using the correlation between front and rear wheel road inputs. In this paper, two main issues were investigated, i.e. the estimation accuracy of the Kalman filter for state variables, and the potential improvements from wheelbase preview. Simulations showed good estimations from the state observer. However, if the wheelbase preview algorithm is incorporated, the estimation accuracy for the additional states significantly decreases as vehicle speed and the corresponding measurement noises increase. Significant benefits from wheelbase preview were further proved, and the available performance improvements of the rear wheel station could be up to 35 per cent. Because of the feasibility and effectiveness of the proposed algorithm, and no additional cost for measurements and sensing needs, wheelbase preview can be a promising algorithm for Kalman filter active suspension system designs.