The suspension system of a vehicle is one of the main components of security and comfort for the driver and the car passengers. Therefore, the use of precise controller is vital to ensure the reliability of these vehicles. This paper presents a novel approach to tuning the proportional-integral-derivative (PID) controller of an active suspension system using the walrus optimization algorithm (WOA). This bioinspired meta-heuristic technique is used to find the optimal gains for the PID controller considering the minimization of the mean absolute tracking error. Systematic rules are defined to drive the optimization procedure of the controller. The system is simulated in three scenarios: a flat road, a speed bump, and a pothole, allowing to evaluate the adaptability of the controller in average traffic conditions. The results of 50 experiments demonstrate the effectiveness of the proposed method in finding the best gains for the controller, resulting in satisfactory performance and minimizing the tracking error to a residual value. A comparison with the classical closed-loop Ziegler & Nichols method is provided, where the proposed method ensures a tracking error reduction of ${9 4. 7 6 \%}$ and a root mean square error reduction of ${9 4. 3 3 \%}$. Additionally, the overshoot during the startup transient regime was reduced by ${9 0. 6 6 \%}$, while also eliminating the settling time of the system when evaluated utilizing the $2 \%$ criteria.
Enhancing quarter car active suspension performance using walrus optimization algorithm-based PID controller
20.10.2024
534649 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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