Active Suspension Control in Vehicle Dynamics
Summary
Active suspension systems employ actuators and control algorithms to actively regulate the interaction between vehicle and road, improving ride comfort, handling stability and safety. Unlike passive or semi-active systems that rely on fixed or limited adjustable damping characteristics, active suspensions generate controlled forces to counteract disturbances from road irregularities, braking and cornering manoeuvres. Key components include sensors for body acceleration, wheel displacement and road preview; actuators, often hydraulic or electromagnetic, that apply precise forces; and real-time control strategies that compute actuator commands to optimise multiple objectives. Advances in sensor technology, computational power and actuator design have led to sophisticated control approaches capable of anticipating road inputs, coordinating multi-degree-of-freedom vehicle dynamics and reducing energy consumption. Contemporary research spans methods such as predictive control, fuzzy logic and machine learning, with applications in passenger cars, commercial vehicles and rail systems. Practical implementations have demonstrated significant reductions in body acceleration, suspension deflection and tyre load variations, thereby enhancing global vehicle performance and passenger experience.
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Active Suspension Control in Vehicle Dynamics publication trend
The graph below shows the total number of articles in active suspension control in vehicle dynamics across all publications each year (not limited to Nature Index journals).
Technical terms
Active suspension: A suspension system that uses actuators to apply forces independently of vehicle motion, actively controlling ride dynamics.
Model predictive control (MPC): An optimisation-based strategy that predicts future system states over a finite horizon to compute control inputs that minimise a cost function.
Preview: Use of sensors or estimators to detect forthcoming road disturbances, enabling anticipatory control actions.
Fuzzy PID control: A hybrid approach combining fuzzy logic for rule-based inference with proportional-integral-derivative algorithms to adapt gain parameters dynamically.
Deep reinforcement learning (DRL): A machine learning technique where an agent learns optimal control policies by interacting with the environment and maximising cumulative rewards.
References
- Regionless Explicit Model Predictive Control of Active Suspension Systems With Preview. IEEE Transactions on Industrial Electronics (2019).
- Adaptive Fuzzy PID Control Strategy for Vehicle Active Suspension Based on Road Evaluation. Electronics (2022).
- Semi-Active Suspension Control Based on Deep Reinforcement Learning. IEEE Access (2020).
- Active suspension in railway vehicles: a literature survey. Railway Engineering Science (2020).
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