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Advanced force control of the 2-axes PAM-based manipulator using adaptive neural networks

Published online by Cambridge University Press:  01 June 2018

Ho Pham Huy Anh*
Affiliation:
Faculty of Electrical and Electronics Engineering (FEEE), HCM City University of Technology, VNU-HCM, Ho Chi Minh City, Vietnam. E-mail: [email protected] Key Laboratory of Digital Control and System Engineering (DCSELAB), Ho Chi Minh City University of Technology, VNU-HCM, Ho Chi Minh City, Vietnam. E-mail: [email protected]
Cao Van Kien
Affiliation:
Faculty of Electrical and Electronics Engineering (FEEE), HCM City University of Technology, VNU-HCM, Ho Chi Minh City, Vietnam. E-mail: [email protected]
Nguyen Thanh Nam
Affiliation:
Key Laboratory of Digital Control and System Engineering (DCSELAB), Ho Chi Minh City University of Technology, VNU-HCM, Ho Chi Minh City, Vietnam. E-mail: [email protected]
*
*Corresponding author. E-mail: [email protected]

Summary

This paper proposes a detailed investigation on the new neural-based feed-forward PID direct force control (FNN-PID-DF) approach applied to a highly nonlinear 2-axes pneumatic artificial muscle (PAM) manipulator in order to ameliorate its force output performance. Founded on the novel inverse neural NARX model dynamically identified to learn well all nonlinear characteristics of the contact force dynamics of the 2-axes PAM-based manipulator, the novel proposed neural FNN-PID-DF force controller is innovatively implemented in order to directly force control the 2-axes PAM robot system used as a rehabilitation device subjected to internal systematic interactions and external contact force deviations. The performance of the experimental tests has proven the advantages and merits of the new force control method compared to the classical PID force control method. The new neural FNN-PID-DF force controller guides the wrist/hand of subject/patient to successfully generate the predefined desired force values.

Type
Articles
Copyright
Copyright © Cambridge University Press 2018 

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