# A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles
## 4 Roll Stability Control in Road Vehicles
### Active Roll Stability Control Technologies  
#### 4.2.1 Single-Actuator Control  
When the rollover prediction system detects that a vehicle is at high rollover risk, the active anti-rollover control system immediately intervenes to adjust the vehicle dynamic response and reduce the rollover risk. Currently, anti-rollover control for road vehicles primarily relies on control techniques such as active braking, active steering, and active suspension. These methods optimize vehicle posture and improve stability by adjusting the longitudinal, lateral, and vertical forces at the tires. Extensive research has been conducted on roll stability control using single actuators. Among these, active braking control can influence the vehicle's yaw moment through brake force distribution (BFD), thereby reducing lateral acceleration and mitigating rollover risk [94]. In addition, differential drive control--by applying torque asymmetrically across the driving wheels--can also generate corrective yaw moments to enhance lateral stability. Both approaches contribute to optimizing the vertical load distribution between the left and right wheels, effectively lowering the vehicle's rollover propensity. For instance, [PERSON] et al. [73] developed a hierarchical control system combining sliding mode ABS, differential braking, and slip ratio identification, which reduced the yaw rate and lateral acceleration by 9-18% in J-turn and fishhook tests. [PERSON] et al. [83] integrated rollover prevention and ABS using LTR-based differential braking, effectively maintaining rollover thresholds during extreme maneuvers without extra sensors. [PERSON] et al. [95] designed a control system for three-axle rescue vehicles using fuzzy PID active suspension and MPC-based braking, which showed superior rollover resistance in step and fishhook tests. Addressing braking failure in steer-by-wire vehicles, [PERSON] et al. [96] proposed a nonlinear LTR and layered control architecture incorporating failure modeling and road adhesion identification, which improved lateral stability by up to 68.18%. [PERSON] et al. [22] proposed an anti-roll control method based on four-wheel independent drive, and designed a sliding mode variable structure controller. Simulation and real-vehicle tests demonstrated that the proposed approach effectively suppressed vehicle roll and yaw motions, thereby enhancing the spatial stability of in-wheel motor electric vehicles on uneven road surfaces. The advantage of active braking and driving lies in their ability to generate a stable yaw moment in a short period, thereby reducing the rollover risk. However, this method may impact the vehicle's longitudinal performance and increase the thermal load on the braking system. Active steering control (ASC) adjusts the steering angles of the front or rear wheels to alter the vehicle yaw motion and lateral force distribution, thereby reducing the lateral acceleration and roll moment, effectively mitigating the rollover risk. Compared to active braking control, active steering allows for smoother intervention in vehicle motion without significantly affecting the longitudinal dynamics, optimizing the roll dynamic response and enhancing driving stability. For example, [PERSON] et al. [70] proposed an MPC-based coordinated control strategy combining active front steering (AFS) and direct yaw moment control (DYC), achieving a 30% improvement in stability and a 25% reduction in path error. [PERSON] et al. [97] integrated AFS with differential braking using a nonlinear MPC controller and understeering gradient model, enhancing robustness and improving stability by 32%. [PERSON] et al. [76] introduced a 3D stability zone based on the lateral speed, yaw rate, and roll angle, improving the average and minimum stability metrics by 23.89% and 50.16% under low-adhesion conditions. Active steering proves to be a precise, non-intrusive method for mitigating roll dynamics through optimal lateral force distribution.