Control Method for Myoelectric Prosthetic Hand’s Grip Force Based on Neural Network and Fuzzy Logic Controller

Abstract:

This paper presents a control method for prosthetic hand's grip force. Firstly, the correlated characters of the SEMG signal are calculated and input into the neural network in order to extract the estimation of muscle strength. Secondly, the estimation of muscle strength and the real-time prosthetic hand’s grip force are input into the designed fuzzy logic controller in order to adjust the motor speed, and indirectly control the grip force. This method could greatly reduce the individual difference of SEMG signal, make it can be used by Non-specific patient, and the experiment demonstrates that the control of prosthetic hand’s grip force is effective.

• Periodical: Advanced Materials Research (Volumes 403-408)
• Pages: 2843-2847
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.403-408.2843
• Online since: November 2011
• Authors: Hua Tao Zhang, Ai Guo Song, Yan Guo, Jia Tong Bao, Kui Qian
• Keywords: Fuzzy Logic, Grip Force, Neural Network (NN), SEMG

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