Abstract The aim of the paper is to propose algorithms for suppressing vibrations of massive components of machinery, e.g., operators' cabins in bucket wheel excavators (BWEs). Repetitive perturbations provide the opportunity to learn a control system to suppress or reduce their influence by applying a repetitive control scheme. A new version of the iterative learning control approach, called iterative learning of optimal control (ILOC), is derived. This approach is able to learn a control signal that approximately minimizes the squared acceleration of a system, e.g., the cabin. The resulting algorithms can be used alone or in parallel with a hybrid version of the classic proportional-derivative (PD) controller. The simulations indicate that the ILOC algorithm provides a large improvement in suppressing quasi-periodic disturbances in comparison to the cases of no control and PD control, while the hybrid version provides even slightly better results. The proposed algorithms are intended to be used with magneto-rheological (MR) dampers as semi-active control devices. Subtle properties of MR dampers, such as their hysteresis, are omitted here.

中文翻译：

---

使用磁流变阻尼器抑制工程机械振动的迭代学习

摘要 本文的目的是提出用于抑制大型机械部件振动的算法，例如斗轮挖掘机 (BWE) 中的操作员舱。重复扰动提供了学习控制系统以通过应用重复控制方案抑制或减少其影响的机会。迭代学习控制方法的新版本，称为最优控制的迭代学习（ILOC），被推导出来。这种方法能够学习近似最小化系统（例如，机舱）的平方加速度的控制信号。由此产生的算法可以单独使用，也可以与经典比例微分 (PD) 控制器的混合版本并行使用。仿真表明，与无控制和 PD 控制的情况相比，ILOC 算法在抑制准周期性扰动方面提供了很大的改进，而混合版本的结果甚至略好一些。所提出的算法旨在与作为半主动控制装置的磁流变 (MR) 阻尼器一起使用。这里省略了 MR 阻尼器的微妙特性，例如它们的滞后。