A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles
Abstract
:1. Introduction
2. Research Method
3. Theoretical Foundations of Roll Stability Control
3.1. Roll Dynamic Model
3.2. Rollover Prediction Index
3.3. Basic Principles of Roll Stability Control
4. Roll Stability Control in Road Vehicles
4.1. Roll State Monitoring and Risk Prediction
4.2. Active Roll Stability Control Technologies
4.2.1. Single-Actuator Control
4.2.2. Integrated Stability Control
4.3. Experimental Validation of Roll Stability for Road Vehicles
4.3.1. HIL Experiment Tests
4.3.2. Real-Vehicle Tests
5. Roll Stability Control in Off-Road Vehicles
5.1. Roll Characteristics of Off-Road Vehicles
5.2. Roll State Evaluation and Rollover Warning
5.3. Active Roll Stability Control Methods
5.3.1. Active Leveling Control
5.3.2. Direct Torque Control
5.3.3. Active Steering
5.4. Typical Roll Stability Experiments for Off-Road Vehicles
6. Comparative Analysis and Future Directions
6.1. Comparisons of Roll Stability Control Between Road and Off-Road Vehicles
6.2. Future Directions
- Future roll state monitoring systems will prioritize real-time, robust roll state sensing through advanced multi-sensor fusion (e.g., IMU, LiDAR, GNSS, vision) and adaptive filtering or nonlinear observers to improve accuracy under dynamic and noisy conditions.
- Future systems will apply deep learning and reinforcement learning to build data-driven rollover prediction models, reducing reliance on complex dynamics while improving adaptability to extreme conditions. V2X integration will enable real-time cooperative risk assessment using shared vehicle and road data.
- Control algorithms will increasingly utilize adaptive learning methods, such as deep reinforcement learning and neural networks, to dynamically adjust to diverse driving conditions. Hybrid modeling (physical + data-driven) will enhance robustness and applicability.
- Future systems will evolve toward coordinated control of suspension, braking, and steering, improving stability in high-risk scenarios. Technologies like steer-by-wire and active chassis systems will support real-time structural adjustments.
- To address computational challenges of high-precision controllers (e.g., MPC), research will focus on reduced-order modeling, control parameter tuning via reinforcement learning, and deployment via edge computing to ensure fast and efficient real-time performance.
- For off-road environments, future work will enhance terrain perception through LiDAR, IMU, and vision fusion. Control strategies will include adaptive suspension, variable track width, and real-time center-of-gravity adjustments to maintain stability on uneven terrain.
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
RSC | Roll stability control |
MPC | Model predictive control |
ESP | Electronic Stability Program |
IMU | Inertial measurement unit |
LTR | Load transfer rate |
TTR | Time to rollover |
HIL | Hardware-in-the-loop |
SMC | Sliding mode control |
LQR | Linear Quadratic Regulator |
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References | Modeling Approach | Application | Analysis Perspective | Simulated Motion | Remarks |
---|---|---|---|---|---|
[64,65] | Nonlinear suspension model with MPC | Active roll stability control | Effects of different loads and steering conditions | Roll, lateral, yaw motion | Developed an MPC-based active anti-roll control strategy to adjust suspension stiffness and damping |
[66,67] | Variable CG height model with lateral motion | Adaptive roll stability control | Impact of different loads and road conditions | Roll, lateral, vertical motion | Designed an adaptive roll stability controller based on variable CG height |
[61,62,68] | Full-vehicle dynamic model with modified CRI | Rollover risk prediction | Influence of CG height and lateral acceleration | Roll, lateral acceleration | Proposed an improved Critical Roll Index (CRI) for rollover risk prediction |
[69,70] | Vehicle stability analysis model with tire parameter estimation | Influence analysis of rollover factors | Effects of tire parameters | Roll, lateral, vertical motion | Verified the effects of tire parameters on rollover stability |
[71,72,73] | Multi-body dynamic model | Heavy-duty vehicle rollover prevention | Structural deformation effects | Roll, yaw, lateral motion | Investigated rollover risk under high-speed cornering with multi-body dynamics |
[74,75] | Integrated roll–yaw model with neural network | Intelligent roll stability control | Data-driven approach for active roll control | Roll, yaw, lateral motion | Utilized neural networks to enhance active roll stability control |
Prediction Index | Evaluation Criteria | Equation | Rollover Threshold | Remarks | References |
---|---|---|---|---|---|
Roll rate () | Rate of change in roll angle, used to assess roll dynamics | No fixed threshold, usually combined with roll angle | Reflects roll tendency, but requires roll angle for rollover assessment | [84,85] | |
Lateral acceleration (ay) | Lateral acceleration of vehicle, indicating rollover tendency | Typically, ay > 0.4 g indicates rollover risk | Suitable for steady-state rollover analysis, but requires suspension considerations | [68] | |
Roll energy | Kinetic and potential energy of vehicle roll motion | Vehicle-dependent, no fixed threshold | Useful for predicting rollover trends, but complex to compute | [14,86] | |
Dynamic stability index (DSI) | Considers multiple factors, such as roll angle, lateral acceleration, and speed | Computed based on multivariable functions | Depends on dynamic modeling, no fixed threshold | Suitable for intelligent vehicle rollover prediction, computationally complex | [80,87] |
Tire lateral force ratio (TLFR) | Ratio of tire’s lateral force to its maximum available lateral force | TLFR > 1 indicates a limit condition, possibly leading to loss of control | Useful for analyzing tire adhesion limits, but sensitive to load variations | [88] | |
CG height variation rate (CGVR) | Dynamic variation rate of vehicle’s center of gravity height | Vehicle-dependent, no fixed threshold | Suitable for uneven terrain analysis, computationally intensive | [89,90] |
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Chen, J.; Wang, R.; Liu, W.; Sun, D.; Jiang, Y.; Ding, R. A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles. Appl. Sci. 2025, 15, 5491. https://doi.org/10.3390/app15105491
Chen J, Wang R, Liu W, Sun D, Jiang Y, Ding R. A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles. Applied Sciences. 2025; 15(10):5491. https://doi.org/10.3390/app15105491
Chicago/Turabian StyleChen, Jie, Ruochen Wang, Wei Liu, Dong Sun, Yu Jiang, and Renkai Ding. 2025. "A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles" Applied Sciences 15, no. 10: 5491. https://doi.org/10.3390/app15105491
APA StyleChen, J., Wang, R., Liu, W., Sun, D., Jiang, Y., & Ding, R. (2025). A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles. Applied Sciences, 15(10), 5491. https://doi.org/10.3390/app15105491