Active suspensions in vehicles can apply a vertical force between the wheel and the vehicle body to reduce vehicle body motion over road obstacles, enhancing ride comfort. To incorporate road profile estimation into active suspension control, this paper proposes a wheelbase-preview predictive controller (WPMPC). The road profile of the front wheel and suspension states are estimated using the augmented Kalman filter method with the control item. Consequently, the estimated profile information from the front wheels is used to obtain the in-wheel base information by shift register. Further, the wheelbase-preview predictive model is established, involving the estimated states and in-wheelbase information. The corresponding WPMPC is formulated with the physical constraints and the dynamic deflection of suspension. The Adams-Simulink co-simulation platform is built. The comparisons are conducted on the random and impact road. The results show that the proposed algorithm has improved the root mean square (RMS) of the acceleration, dynamic deflection, and relative dynamic load of rear suspension by more than 3.68%, 6.91%, and 13.53%, compared to the traditional MPC and LQR.
Yang et al. (2026) studied this question.