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

Based on a half-vehicle model, an algorithm is proposed for a Kalman filter optimal active vehicle suspension system using the correlation between fro nt and rear wheel road inputs. In this paper, two main issues were investig ated, 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 previe w algorithm is incorporated, the estimation accuracy for the additional sta tes significantly decreases as vehicle speed and the corresponding measurem ent noises increase. Significant benefits from wheelbase preview were furth er proved, and the available performance improvements of the rear wheel sta tion could be up to 35 per cent. Because of the feasibility and effectivene ss of the proposed algorithm, and no additional cost for measurements and s ensing needs, wheelbase preview can be a promising algorithm for Kalman fil ter active suspension system designs.