Neural Compensation Technique for Fuzzy Controlled Humanoid Robot Arms : Experimental Studies

Deok-Hee Song, Seul Jung
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引用次数: 3

Abstract

In this paper, a neural network compensation technique is proposed for a fuzzy controlled humanoid robot arm. The robot arm is controlled by fuzzy controllers, and then neural network controller is added to improve the performance for system variations by modifying fuzzy rules. The overall structure forms a neuro-fuzzy controlled system, in the sense that the proposed control algorithm can have the effect of changing fuzzy rules. Experimental studies have been carried out to test the performance of the proposed control algorithm. Experimental results have confirmed that the proposed neural network compensation scheme for fuzzy controlled systems works best among several control methods.
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模糊控制类人机械臂神经补偿技术的实验研究
针对模糊控制的仿人机械臂,提出了一种神经网络补偿技术。采用模糊控制器对机械臂进行控制,然后加入神经网络控制器,通过修改模糊规则来提高系统变化的性能。整体结构形成一个神经模糊控制系统,从某种意义上说,所提出的控制算法可以具有改变模糊规则的效果。实验研究已经进行了测试所提出的控制算法的性能。实验结果表明,所提出的神经网络补偿方案在多种控制方法中效果最好。
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