Preview Control of Active Vehicle Suspensions Based on a State and Input Estimator

Wheelbase preview control system that uses state and input estimator to reconstruct state and preview information is proposed. Conventional preview control systems use Kalman-Bucy filters of augmented system, which is composed of dynamics of a vehicle and a road profile for estimation scheme. Use of road model makes control performance sensitive to model errors that are inevitable in real applications. Compared with the conventional preview control systems, the proposed control system adopts a state and input estimator to estimate state and road input simultaneously. The state and input estimator does not require a road model, which makes it not robust to road model errors. However, the state and input estimator is sensitive to measurement noises, since it uses inverse dynamics of a system to estimate unknown inputs. To cope with the susceptibility of the estimator to measurement noises, we will develop a state and input estimator that minimizes the noise effect on control performance. We demonstrate performance improvement of the proposed control system over the conventional ones through computer simulations of a half car model.