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Adaptive Neural Compliant Force-Position Control of Serial PAM Robot

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Abstract

This paper proposes the novel adaptive neural network (ADNN) compliant force/position control algorithm applied to a highly nonlinear serial pneumatic artificial muscle (PAM) robot as to improve its compliant force/position output performance. Based on the new adaptive neural ADNN model which is dynamically identified to adapt well all nonlinear features of the 2-axes serial PAM robot, a new hybrid adaptive neural ADNN-PID controller was initiatively implemented for compliant force/position controlling the serial PAM robot system used as an elbow and wrist rehabilitation robot which is subjected to not only the internal coupled-effects interactions but also the external end-effecter contact force variations (from 10[N] up to critical value 30[N]). The experiment results have proved the feasibility of the new control approach compared with the optimal PID control approach. The novel proposed hybrid adaptive neural ADNN-PID compliant force/position controller successfully guides the upper limb of subject to follow the linear and circular trajectories under different variable end-effecter contact force levels.

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Acknowledgements

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 107.01-2015.23 and by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number B2016-20-03.

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Authors and Affiliations

  1. FEEE, DCSELAB, HCM City University of Technology, VNU-HCM, Ho Chi Minh City, Vietnam

    Ho Pham Huy Anh & Cao Van Kien

  2. Faculty of Electronic Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Nguyen Ngoc Son

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  1. Ho Pham Huy Anh
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  2. Nguyen Ngoc Son
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  3. Cao Van Kien
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Correspondence to Ho Pham Huy Anh.

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Huy Anh, H.P., Son, N.N. & Van Kien, C. Adaptive Neural Compliant Force-Position Control of Serial PAM Robot. J Intell Robot Syst 89, 351–369 (2018). https://doi.org/10.1007/s10846-017-0570-1

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  • DOI: https://doi.org/10.1007/s10846-017-0570-1

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