In this article, a new methodology, hybrid genetic algorithm GA, algorithm SPEA/R with Deep Neural Network (HDNN&SPEA/R). This combination gave computing time much faster than computing time when using genetic algorithms SPEA/R. On the other hand, this combination also significantly reduces the number of samples needed for the training of deep artificial neural networks. This is the task of finding out an optimal set that changes with the engine velocity of multi-objective optimization involving 12 simultaneous optimization goals: proportional P, integral I, derivative D, additional integration n and differentiation orders m factor, displacement amplification coefficient KDloop, acceleration amplification coefficient KAloop in two controllers acceleration and displacement to enhance the ride comfort. This article has provided a control algorithm of a Cascade FOPID controller to control the acceleration and displacement of the mount. Besides, the article also offers solutions to optimize the 12 simultaneous parameters of the two controllers by the new hybrid method HDNN&SPEA/R and suitable for the speed of rotation of the engine. To increase the safety factor in operation, we use magnetorheological dampers (MR) in a powertrain mounting system and a continuous state damper controller that calculates the input voltage to the damper coil. The results of this control method are compared with traditional PID systems, optimal PID parameter adjustment using genetic algorithms (GA) and passive drive system mounts. The results are tested in both time and frequency domains, to verify the success of the proposed Cascade FOPID algorithm. The results show that the proposed Cascade FOPID controller of the MR engine mounting system gives very good results in comfort and softness when riding compared to other controllers. This proposal has reduced 335 hours for optimal computation time and reduce vibration a lot.

中文翻译：

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新的混合SPEA / R深度学习算法可在半车动态模型的动力总成安装系统控制中根据发动机转速预测级联FOPID控制器的优化参数

本文介绍了一种新的方法，即混合遗传算法GA，具有深度神经网络的算法SPEA / R（HDNN＆SPEA / R）。这种组合使计算时间比使用遗传算法SPEA / R的计算时间快得多。另一方面，这种组合也大大减少了训练深度人工神经网络所需的样本数量。这项任务是找出一个最佳集合，该集合会随着涉及12个同时优化目标的多目标优化的引擎速度而变化：比例P，积分I，导数D，附加积分n和微分阶数m因子，位移放大系数KDloop，加速度放大系数KAloop在两个控制器中加速和位移，以提高乘坐舒适性。本文提供了一种级联FOPID控制器的控制算法，以控制底座的加速度和位移。此外，本文还提供了通过新的混合方法HDNN＆SPEA / R优化两个控制器的12个同时参数的解决方案，并适合于发动机的转速。为了提高运行中的安全系数，我们在动力总成安装系统中使用了磁流变阻尼器（MR），并使用了一个连续状态的阻尼器控制器来计算阻尼器线圈的输入电压。将该控制方法的结果与传统PID系统，使用遗传算法（GA）的最佳PID参数调整以及无源驱动系统支架进行了比较。在时域和频域对结果进行测试，以验证所提出的Cascade FOPID算法是否成功。结果表明，与其他控制器相比，拟议的MR发动机安装系统的Cascade FOPID控制器在乘坐时的舒适性和柔软性方面具有很好的效果。该建议减少了335小时的最佳计算时间，并大大减少了振动。