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Decentralized Neuro-Fuzzy Controller Design Using Decoupled Sliding-Mode Structure for Two-Dimensional Inverted Pendulum

机译:使用解耦滑模结构的二维倒立摆分散神经模糊控制器设计

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In this paper, a new method has been proposed to control a two-dimensional inverted pendulum. First, the system of a two-dimensional inverted pendulum is divided into two subsystems using decentralized control theory. Then, using decoupling method, each subsystem is decoupled into two surfaces for applying sliding-mode control. Next, this controller has been used to train two neuro-fuzzy ANFIS (Adaptive-Network-Based Fuzzy Inference System) networks. Due to the high accuracy of the ANFIS networks, these two networks can learn the controlling abilities of the teacher. Each trained network controls its own subsystem as a local controller. The trained networks not only have the properties of their teacher, but also due to the parallel processing property of the ANFIS networks, they response much faster than their teacher. Moreover, the neuro-fuzzy controller doesn't need any model of the plant or its parameters. Simulation results show a high performance for the proposed method as compared to the existing methods.
机译:在本文中,已经提出了一种控制二维倒立摆的新方法。首先,使用分散的控制理论将二维倒立摆的系统分成两个子系统。然后,使用去耦方法,每个子系统被分离成两个表面以用于施加滑模控制。接下来,该控制器已用于培训两个神经模糊的ANFIS(基于自适应网络的模糊推理系统)网络。由于ANFIS网络的高精度,这两个网络可以学习教师的控制能力。每个训练的网络将其自己的子系统控制为本地控制器。训练有素的网络不仅具有他们的教师的属性,而且由于ANFIS网络的并行处理属性,它们比他们的老师更快地回复。此外,神经模糊控制器不需要任何植物或其参数模型。与现有方法相比,仿真结果表明了所提出的方法的高性能。

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