Guo Qinghe, Wang Mengchao, Liu Renjun, Chen Yurong, Wang Shenghuai, Wang Hongxia
School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan, Hubei, China.
Key Laboratory of Automotive Power Train and Electronics, Hubei University of Automotive Technology, Shiyan, Hubei, China.
PLoS One. 2025 Jun 10;20(6):e0323066. doi: 10.1371/journal.pone.0323066. eCollection 2025.
Traditional PID control faces challenges in addressing parameter uncertainty and nonlinearity in active suspension electrohydraulic servo actuators, leading to suboptimal performance. To address these challenges, a fractional-order PID (FOPID) controller optimization method based on the Multi-Strategy Improved Beluga Whale Optimization (MSIBWO) algorithm is proposed. Simulation results in MATLAB/Simulink demonstrate that the MSIBWO-FOPID controller significantly outperforms traditional PID and BWO-FOPID controllers in force tracking and robustness. For step input, the rise time and the root mean square error(RMSE) are reduced by 66.7[Formula: see text] and 70.3[Formula: see text], respectively, compared to BWO-FOPID. For sine inputs, the system achieves better disturbance rejection and higher precision. Using a half-car model, the MSIBWO-FOPID controller improves ride comfort significantly. Under random road excitation, the RMSE values of the vehicle body's vertical acceleration and pitch angle acceleration are reduced by 51.7[Formula: see text] and 13.1[Formula: see text], respectively, compared to passive suspension, outperforming both PID and BWO-FOPID controllers.
传统的PID控制在解决主动悬架电液伺服执行器中的参数不确定性和非线性问题上面临挑战，导致性能欠佳。为应对这些挑战，提出了一种基于多策略改进白鲸优化（MSIBWO）算法的分数阶PID（FOPID）控制器优化方法。MATLAB/Simulink中的仿真结果表明，MSIBWO-FOPID控制器在力跟踪和鲁棒性方面明显优于传统PID和BWO-FOPID控制器。对于阶跃输入，与BWO-FOPID相比，上升时间和均方根误差（RMSE）分别降低了66.7[公式：见原文]和70.3[公式：见原文]。对于正弦输入，系统实现了更好的抗干扰能力和更高的精度。使用半车模型时，MSIBWO-FOPID控制器显著提高了乘坐舒适性。在随机道路激励下，与被动悬架相比，车身垂直加速度和俯仰角加速度的RMSE值分别降低了51.7[公式：见原文]和13.1[公式：见原文]，优于PID和BWO-FOPID控制器。
