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Nonlinear control of triple inverted pendulum based on T-S cloud inference network

机译:基于T-S云推理网络的三重倒立摆的非线性控制

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The triple inverted pendulum is a nonlinear, dynamic and unsteady system. The traditional control methods of triple inverted pendulum have limited control accuracy and slow responding. This kind of pendulum system is difficult to control due to the inherent instability and nonlinear behavior. In this paper, GA-RBF-ARX model is applied to model triple inverted pendulum based on its input/output data. According to the nonlinear identification model, a novel method via T-S cloud inference network controller optimized by genetic algorithm (GA) is proposed. T-S cloud inference network is constituted by T-S fuzzy neural network and the cloud model. Therefore, the rapid of fuzzy logic and the uncertain of cloud model for processing data are both taken into account. What's more, GA possesses global optimization characteristics and good parallel design structure. Compared with the simulation recognition results of T-S fuzzy neural network controller, T-S cloud reference network controller has strong robust and fast calculate speed. So, T-S cloud inference network by GA is an effective method in control.
机译:三重倒立摆是一个非线性,动态和不稳定的系统。传统的三重倒立摆控制方法控制精度有限,响应速度较慢。由于固有的不稳定性和非线性行为,这种摆系统难以控制。本文将GA-RBF-ARX模型基于其输入/输出数据应用于三重倒立摆模型。根据非线性辨识模型,提出了一种通过遗传算法(GA)优化的T-S云推理网络控制器的新方法。 T-S云推理网络由T-S模糊神经网络和云模型组成。因此,模糊逻辑的快速性和云模型对数据处理的不确定性都被考虑在内。而且,GA具有全局优化特性和良好的并行设计结构。与T-S模糊神经网络控制器的仿真识别结果相比,T-S云参考网络控制器具有较强的鲁棒性和较快的计算速度。因此,基于遗传算法的T-S云推理网络是一种有效的控制方法。

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