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Internal Model Control and Experimental Study of Ankle Rehabilitation Robot

Published online by Cambridge University Press:  29 July 2020

Lan Wang ,
Ying Chang Open the ORCID record for Ying Chang [Opens in a new window]  and
Haitao Zhu
Lan Wang
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang Province, PRChina E-mails: [email protected], [email protected]
Ying Chang*
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang Province, PRChina E-mails: [email protected], [email protected]
Haitao Zhu
Affiliation:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, Heilongjiang Province, PRChina E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

In the present work, the ankle rehabilitation robot (ARR) dynamic model that implements a new series of connection control strategies is introduced. The dynamic models are presented in this regard. This model analyzes the robot LuGre friction model and the nonlinear disturbance model. To improve the ARR system’s rapidity and robustness, a composite 2-degree of freedom (2-DOF) internal model control (IMC) controller is presented. The control performance of the compound 2-DOF IMC controller is simulated and analyzed in the present work. The simulation shows that the composite 2-DOF IMC controller has high following performance. For practical testing purposes, 1-DOF passive training and predetermined trajectory following have been completed for different swing amplitudes and frequencies. Moreover, the thrust and tension torque of the robotic dynamic and static loading characteristics are studied in active control mode. The experimental results show the effectiveness of passive training of the given trajectory and impedance training active control strategy. This paper gives the specific functions of ARR.

Keywords

Ankle rehabilitation robotIMC controlImpedance trainingNonlinearityDynamic model
Type
Articles
Information
Robotica , Volume 38 , Issue 5 , May 2020 , pp. 940 - 956
Copyright
© Cambridge University Press 2019

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