Roll–Vertical Coupled Roll State Estimation and Coordinated Control for Active Suspension Vehicles

Roll motion induced by steering maneuvers and vertical vibration excited by road unevenness are strongly coupled in active suspension vehicles. Neglecting this coupling may deteriorate the performance of coordinated chassis control and compromise both roll stability and ride comfort. To improve roll stability and ride comfort under combined steering and road excitation conditions, this paper develops a roll–vertical coupled control framework. First, a nine-degree-of-freedom roll–vertical coupled vehicle model is established by integrating lateral–yaw dynamics, sprung mass heave motion, roll and pitch motion, and four unsprung mass vertical dynamics. Second, an adaptive square root cubature Kalman filter (ASRCKF) is designed to estimate key roll states, including the roll angle and roll rate. The square root structure improves numerical stability, while the Sage–Husa adaptive estimator updates the measurement noise covariance online using the innovation sequence. Third, a load transfer ratio-based rollover risk assessment method and a model predictive control (MPC)-based active suspension controller are introduced to realize coordinated roll–vertical control. Finally, the proposed framework is validated using a MATLAB/Simulink–CarSim co-simulation platform. The results demonstrate that the proposed method effectively improves vehicle roll stability and vertical ride performance under complex driving conditions.