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The accuracy of state estimation and optimal control for controllable suspension system is a challenging task for the vehicle suspension system under various road excitations. How to effectively acquire suspension states and choose the reasonable control algorithm become a hot topic in both academia and industry. Uncertainty is unavoidable for the suspension system, e.g., varying sprung or unsprung mass, suspension damping force or spring stiffness. To tackle the above problems, a novel observer-based control approach, which combines adaptive unscented Kalman filter (AUKF) observer and model predictive control (MPC), is proposed in the paper. A quarter semi-active suspension nonlinear model and road profile model are first established. Secondly, using the road classification identification method based on system response, an AUKF algorithm is employed to estimate accurately the state of suspension system.