# A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles
## 1 Introduction
The MPC framework allows for the simultaneous consideration of roll stability, yaw control, and trajectory tracking, enabling more precise dynamic adjustments [4; 24; 25]. For instance, [PERSON] et al. [11] addressed the conflict between path tracking and roll stability control during emergency avoidance maneuvers by proposing a cooperative control method based on closed-loop feedback Nash game theory. Simulation and hardware-in-the-loop experiments showed that this controller improved path tracking accuracy by 23% and roll stability by 35%, effectively solving the coordination optimization problem of the two control systems. [PERSON] et al. [26] proposed a yaw-roll coordination control strategy based on dynamic safety demands for heavy vehicles in complex road conditions, using a dynamic weight model predictive control approach based on stochastic integrated double Q-learning. Simulation and experimental results demonstrated that this strategy could adjust control weights in real time, improving vehicle stability by 32% in dynamic environments and effectively addressing various instability risks. However, despite the advancements in existing technologies, several challenges remain, including uncertainty in rollover risk prediction, the robustness of control systems under complex conditions, and the impact of actuator response delays on control performance. Moreover, with the development of new energy and intelligent vehicles, traditional control methods need to be deeply integrated with intelligent chassis and motor drive systems to meet the evolving needs of future vehicles. Therefore, a systematic review and analysis of the current theories and methods of roll stability control for road vehicles is necessary to identify the limitations of existing technologies and explore future development directions. The development of roll stability control technologies for off-road vehicles exhibits significant differences from that for road vehicles, primarily due to the challenges posed by complex terrain, dynamic load variations, and diverse operational modes [27; 28; 29]. In contrast to road vehicles, which focus primarily on rollover risks during high-speed driving, off-road vehicles (such as agricultural machinery, construction vehicles, and mining transport equipment) face multiple uncertainties, including rugged terrain, uneven load distribution, and steep slope operations. These factors limit the effectiveness of traditional passive rollover prevention measures in practical applications [30; 31; 32]. Furthermore, with the widespread adoption of electric drive technologies in off-road vehicles [33; 34; 35; 36], new challenges have emerged in the roll stability control of new-energy agricultural machinery. In recent years, as intelligent perception and active control technologies have evolved, the roll stability control of off-road vehicles has increasingly shifted towards adaptive, predictive, and intelligent systems. Firstly, breakthroughs in real-time sensing technologies have laid the foundation for roll stability control in off-road vehicles. High-precision inertial measurement units (IMUs), LiDAR, GPS, and terrain sensors are widely used to monitor vehicle posture, tilt angles, and terrain undulations in real time. Combined with ground contact force estimation techniques, these sensors enable accurate evaluation of rollover risks [37; 38; 39; 40; 41]. Active steering and active suspension systems have also been gradually applied to off-road vehicles, enabling real-time adjustments to actuator outputs based on vehicle posture and terrain information, in order to actively suppress roll risks [42]. Moreover, cooperative control strategies related to rollover prevention have been widely implemented in off-road vehicles, facilitating proactive optimization of vehicle posture [43]. For example, [PERSON] et al. [44] addressed the issue of coordinated path tracking and rollover prevention control for autonomous mining trucks operating in complex terrain.