Simultaneous estimation of state and unknown road roughness input for vehicle suspension control system based on discrete Kalman filter

This study presents an improved discrete Kalman filter for simultaneously estimating both all state variables and the unknown road roughness input for a vehicle suspension control system that plays a key role in the ride quality and handling performance while driving the vehicle. The suspension system is influenced by the road roughness input, which causes undesirable vibrations associated with vehicle instability. It is therefore important to estimate the road roughness and state variables information when designing the model-based controller for the vehicle suspension control system associated with the vehicle’s vertical dynamics. However, the implementation of conventional estimation theories for the suspension control system is challenging because the road roughness acts as an unknown input and is difficult to be measured or estimated while driving. This study presents an improved Kalman filter with unknown input, which can simultaneously estimate the state variables and road roughness without any prior information about the vehicle suspension control system. The proposed road roughness input estimator is evaluated by using an in-vehicle test bed with a laser-type profilometer. Finally, the state estimation performance of the proposed estimator for a vehicle suspension control system is validated by using CarSim software.