基于2型模糊逻辑和神经网络的非线性对象自适应模型控制新方法

P. Melin, O. Castillo
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引用次数: 35

摘要

本文利用2型模糊逻辑和神经网络描述了非线性对象的自适应模型控制。首先,介绍了自适应模型控制的一般概念。其次,描述了使用2型模糊逻辑进行自适应控制。第三,提出了一种神经模糊方法来学习模糊系统的参数进行控制。用一个特定的非线性对象来测试混合方法的自适应控制效果。实验以特定植物为试验台。我们所考虑的非线性植物是“Pendubot”,它是一种类似于双连杆机械臂的非线性植物。二类模糊逻辑控制方法在精度和效率上都取得了良好的效果。
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A new method for adaptive model-based control of non-linear plants using type-2 fuzzy logic and neural networks
We describe in this paper adaptive model-based control of non-linear plants using type-2 fuzzy logic and neural networks. First, the general concept of adaptive model-based control is described. Second, the use of type-2 fuzzy logic for adaptive control is described. Third, a neuro-fuzzy approach is proposed to learn the parameters of the fuzzy system for control. A specific non-linear plant is used to test the hybrid approach for adaptive control. A specific plant was used as test bed in the experiments. The non-linear plant that was considered is the "Pendubot", which is a non-linear plant similar to the two-link robot arm. The results of the type-2 fuzzy logic approach for control were good, both in accuracy and efficiency.
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