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Fuzzy iterative learning control strategy for powered ankle prosthesis

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Abstract

A fuzzy iterative learning control strategy for powered ankle prosthesis is presented in this paper. It is used to solve the problem which is the deviation of movement trajectory between powered ankle prosthesis and human ankle joint during walking. The powered ankle prosthesis is a nonlinear and strongly coupled complex system. Traditional fuzzy control and iterative learning control (ILC) as powered ankle prosthesis controllers have their own deficiencies. Fuzzy iterative learning control strategy integrates the two controllers’ advantages. In the initial stage of control, the fuzzy control is used. The outputs of fuzzy control as the initial value of ILC. In the next stage of control, ILC is used. The dynamic model of powered ankle prosthesis is established in this paper. Simulation results indicate that fuzzy iterative learning control strategy can substantially improve control efficient, and needs two iterations to get maximum tracking absolute error ε ≤ 1°.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant (61603284).

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Authors and Affiliations

  1. School of Automation, Wuhan University of Technology, Wuhan, 430070, People’s Republic of China

    Kaiyang Yin, Muye Pang, Kui Xiang, Jing Chen & Shenpei Zhou

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  1. Kaiyang Yin
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Correspondence to Muye Pang.

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Yin, K., Pang, M., Xiang, K. et al. Fuzzy iterative learning control strategy for powered ankle prosthesis. Int J Intell Robot Appl 2, 122–131 (2018). https://doi.org/10.1007/s41315-018-0047-9

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