基于小波神经网络的极限搬运外骨骼机器人自适应控制

Xiuxia Yang, Lihua Gui, Zhiyong Yang, W. Gu
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引用次数: 12

摘要

利用小波神经网络,开发了一种以两个小波神经网络分别作为控制器和动力学模型辨识器的下肢负重外骨骼机器人自适应控制系统。由于小波神经网络具有近似非线性函数的能力和良好的时频局部化特性,该系统可以更精确地识别非线性系统的动态特性,并可以映射更复杂的控制策略。结果表明,该控制系统比基于普通控制器的控制系统更有效,外骨骼跟踪精度高,操作人员感受到的扭矩很小。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lower Extreme Carrying Exoskeleton Robot Adative Control Using Wavelet Neural Networks
Using the wavelet neural networks, an adaptive control system, with two wavelet neural networks as controller and dynamics model identifier respectively, is developed for lower extreme carrying exoskeleton robot. Because the wavelet neural networks have the ability to approximate nonlinear functions and good advantage of time-frequency localization properties, this system can identify nonlinear system dynamic characters more precisely, and can map more complex control strategies. Results show that this control system is more effective than those based on normal controller, where the exoskeleton tracking precision is high and the operator feels very little torque.
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