Active Suspension System Control Strategies

Active suspension systems represent a major advance over passive and semi-active setups by actively injecting energy into the suspension to counteract external disturbances from the road surface. The principal objectives are to enhance ride comfort, improve road holding and handling stability, and maintain safe suspension travel under diverse operating conditions. Control strategies range from classical proportional–integral–derivative schemes to advanced robust and adaptive methods. H∞ control frameworks seek to attenuate the worst-case impact of disturbances, while sliding mode controllers offer robustness against model uncertainties and nonlinearities. Fuzzy logic and neural-network techniques provide adaptive approximation of complex dynamics, enabling real-time compensation for varying vehicle load, actuator nonlinearities and unknown external inputs. Model predictive control and command filtered backstepping extend these ideas by predicting future road profiles and managing actuator constraints, respectively. Multi-objective designs address the trade-offs among sprung mass acceleration, suspension deflection and tyre–road contact forces. Recent advances integrate event-triggered approaches and delay-robust observers to accommodate networked communication and sensor faults. Applications span from passenger cars and heavy vehicles to rail vehicles and seat suspension, underscoring the global significance of these developments for road safety and comfort.

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Active Suspension System Control Strategies publication trend

The graph below shows the total number of articles in active suspension system control strategies across all publications each year (not limited to Nature Index journals).

Technical terms

Active suspension: A system that uses actuators to inject energy into the suspension to counteract road disturbances and improve comfort and stability.

H∞ control: A robust control method that minimises the worst-case gain from disturbances to outputs, ensuring performance under uncertainty.

Sliding mode control: A nonlinear control technique that drives system trajectories onto a predefined sliding surface to achieve robustness against model variations.

Takagi–Sugeno model: A fuzzy modelling framework that represents nonlinear systems as a weighted combination of linear subsystems.

Dynamic surface control: A backstepping-based approach that uses intermediate filters to avoid computational complexity in virtual control signal derivatives.

Lyapunov stability: A mathematical concept ensuring that system energy or a suitable function decreases over time, guaranteeing asymptotic convergence to equilibrium.

References

1. Robust fuzzy delayed sampled-data control for nonlinear active suspension systems with varying vehicle load and frequency-domain constraint. Nonlinear Dynamics (2021).
2. Multi-Objective Robust Control for Vehicle Active Suspension Systems via Parameterized Controller. IEEE Access (2019).
3. Adaptive Dynamic Surface Control for Active Suspension With Electro-Hydraulic Actuator Parameter Uncertainty and External Disturbance. IEEE Access (2020).