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浙江大学学报(工学版)
J4  2010, Vol. 44 Issue (7): 1373-1376    DOI: 10.3785/j.issn.1008973X.2010.07.025
    
Adaptive control for intelligent lower limb prosthesis based on
neural network
MA Yu-liang1, XU Wen-liang1, MENG Ming1, LUO Zhi-zeng1, YANG Jia-qiang2
1. School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; 2. College of Electrical Engineering,
Zhejiang University, Hangzhou 310027, China
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Abstract  

The knee joint of lower limb prosthesis is a damp system with high nonlinearity, timevarying and strong coupling, and the traditional control method can hardly achieve good performance. Aimed at the problem, a neural network (NN)based model reference adaptive control method was proposed based on the learning vector quantization (LVQ) neural network. Based on an appropriate reference model and an adaptive algorithm, the current control variable was calculated by using the error between the reference model output and the actual system output in order to control the intelligent lowerlimb prosthesis and achieve the adaptive control. The method does not require the transformation of performance criteria, and is fast and easy to implement. Simulation results showed the validity of the method.

Published: 01 July 2010
CLC:  TP 13  
  TP 183  
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Cite this article:

MA Yu-Liang, XU Wen-Liang, MENG Meng, LUO Zhi-Ceng, YANG Jia-Jiang. Adaptive control for intelligent lower limb prosthesis based on
neural network. J4, 2010, 44(7): 1373-1376.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008973X.2010.07.025     OR     http://www.zjujournals.com/eng/Y2010/V44/I7/1373


基于神经网络的智能下肢假肢自适应控制

下肢假肢的膝关节是一种具有高度非线性、时变、强耦合的阻尼系统，传统控制方法很难达到良好控制效果.针对这一问题,提出将神经网络(NN)应用于下肢假肢控制.以学习矢量量化（LVQ）神经网络为基础,提出神经网络模型参考自适应控制方法.该方法通过选择适当的参考模型和自适应算法,利用参考模型输出与实际系统输出之间的误差信号,由自适应算法计算当前的控制量以控制智能下肢假肢,达到自适应控制的目的.该方法不需要进行性能指标的变换,容易实现且自适应速度快,仿真结果表明了该方法的有效性.

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