In order to reduce vibration and increase ride comfort, this article utilizes a system of quarter-car suspension integrated with a Fuzzy PID controller. To build and improve the Fuzzy PID controller for the semi-active suspension system used in quarter cars, using a novel meta-heuristic technique known as Grey Wolf Optimizer (GWO). Here the magnetorheological damper (MR) fluid with the Fuzzy PID controller was examined to optimize using the GWO algorithm. With the GWO technique and the integral of time absolute error (IAE) as a fitness function, the three gain parameters of the Fuzzy PID controller – Kp, Ki, and Kd– have been optimally set. The suggested approach has additional advantages for the optimization of functions with three variables, including simplicity in implementation, quick convergence traits, and superior computational capabilities. This work is significant, to the best of the author’s knowledge there is no optimization method using GWO to online tune a Fuzzy PID controller for a semi-active suspension system. The optimal output parameters of the controller can be updated online in real-time by GWO. The performance of the proposed controller was examined by assessing the root mean square (RMS) values and peak-to-peak (PTP) values of body displacement and body acceleration under various road profiles. To ensure that the intelligent controller was of the highest caliber, an online test rig was constructed. Results from simulations and online experiments demonstrated that the Fuzzy GWO PID controller significantly improved ride comfort under a variety of road conditions when compared to the Fuzzy PID controller and passive suspension system.

中文翻译：

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四分之一车半主动悬架系统灰狼优化模糊PID控制器在线优化整定

为了减少振动并提高乘坐舒适性，本文采用了集成了模糊PID控制器的四分之一车悬架系统。使用称为灰狼优化器 (GWO) 的新颖元启发式技术，构建和改进四分之一车半主动悬架系统的模糊 PID 控制器。这里使用 GWO 算法对带有模糊 PID 控制器的磁流变阻尼 (MR) 流体进行了检查以进行优化。利用 GWO 技术和时间绝对误差积分 (IAE) 作为适应度函数，模糊 PID 控制器的三个增益参数 – Kp, K我, 和 Kd– 已进行最佳设置。所提出的方法对于具有三个变量的函数的优化具有额外的优势，包括实现简单、快速收敛特性和卓越的计算能力。这项工作意义重大，据作者所知，还没有使用 GWO 在线调整半主动悬架系统模糊 PID 控制器的优化方法。GWO可以实时在线更新控制器的最优输出参数。通过评估各种道路剖面下车身位移和车身加速度的均方根（RMS）值和峰峰值（PTP）值来检查所提出的控制器的性能。为了确保智能控制器具有最高水准，搭建了在线测试装置。仿真和在线实验结果表明，与模糊PID控制器和被动悬架系统相比，模糊GWO PID控制器在各种路况下显着提高了乘坐舒适性。