In this paper, a new control method is presented for the 4-poster test systems. The primary aim of the paper is to improve the convergence speed and decrease the error rate for model-based iterative learning control (ILC), a widely used method as a tracking control. First, the dynamic equations of the system are generated, and the control problem is formulated. Then, an inverse model of the system is established directly through the adaptive neuro-fuzzy inference system (ANFIS) with auxiliary parameter (piston position) as a serial combination of two sub-models. In order to construct a neuro-fuzzy ILC (NFILC) structure, these sub-models are integrated into the neuro-fuzzy inverse controller (NFIC). Because of this new structure, the modified ILC rule has two layers. In the first layer, the controlled parameter, namely, the acceleration is iterated, whereas, in the second layer, the auxiliary parameter is iterated. The outcomes of the proposed control method are scrutinized by testing through a numerical simulation. Finally, it is demonstrated that the modified ILC rule dramatically increase the convergence speed and reduce the final error rate.

中文翻译：

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4-poster 试验台的神经模糊迭代学习控制

在本文中，提出了一种用于 4 柱测试系统的新控制方法。本文的主要目的是提高收敛速度并降低基于模型的迭代学习控制 (ILC) 的错误率，ILC 是一种广泛使用的跟踪控制方法。首先，生成系统的动态方程，并制定控制问题。然后，将辅助参数（活塞位置）作为两个子模型的串行组合，通过自适应神经模糊推理系统（ANFIS）直接建立系统的逆模型。为了构建神经模糊ILC（NFILC）结构，将这些子模型集成到神经模糊逆控制器（NFIC）中。由于这种新结构，修改后的 ILC 规则有两层。在第一层，对受控参数，即加速度进行迭代，而在第二层，辅助参数是迭代的。所提出的控制方法的结果通过数值模拟进行测试。最后，证明修改后的 ILC 规则显着提高了收敛速度并降低了最终错误率。