Vehicle suspension systems play a critical role in enhancing ride comfort, road handling, and safety, especially under rough road conditions. However, conventional passive and PID-controlled systems often struggle to maintain optimal performance due to nonlinear dynamics and varying road disturbances. Motivated by these limitations, this study aims to develop a robust and adaptive control strategy to improve suspension performance. An enhanced Particle Swarm Optimization (PSO) algorithm is applied to tune a Fractional-Order Proportional-Integral-Derivative (FOPID) controller, addressing the parameter tuning challenges of traditional methods. A quarter-vehicle active suspension model was established in MATLAB/Simulink, and the controller was optimized using a fitness function based on vehicle body acceleration, suspension working space, and dynamic tire displacement. Simulation results demonstrate that the proposed PSO-FOPID controller achieves significant improvements, reducing these performance indicators by 73.25%, 71.78%, and 69.17% compared to a passive suspension system, and outperforming a Model Predictive Control (MPC) strategy by 40.89%, 16.67%, and 69.17%, respectively. Real-vehicle experiments further confirm the controller’s robustness and adaptability under complex road conditions, validating its potential for improving ride comfort, stability, and suspension effectiveness.
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