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Research Papers

Adaptive Impedance Control of Parallel Ankle Rehabilitation Robot

[+] Author and Article Information
Prashant K. Jamwal

Department of Electrical and
Electronics Engineering,
Nazarbayev University,
53 Kabanbay Batyr Avenue,
Astana 010000, Kazakhstan
e-mail: prashant.jamwal@nu.edu.kz

Shahid Hussain

School of Mechanical,
Materials, Mechatronic and
Biomedical Engineering,
University of Wollongong,
Northfields Avenue,
Wollongong, NSW 2522, Australia
e-mail: shussain@uow.edu.au

Mergen H. Ghayesh

School of Mechanical Engineering,
University of Adelaide,
Adelaide, SA 5005, Australia
e-mail: mergen.ghayesh@adelaide.edu.au

Svetlana V. Rogozina

Department of Rehabilitation,
Institute for Scientific Research of
Traumatology and Orthopedics,
Astana 010000, Kazakhstan
e-mail: svetlanarogozina@yahoo.com

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received December 21, 2016; final manuscript received March 31, 2017; published online July 20, 2017. Assoc. Editor: Evangelos Papadopoulos.

J. Dyn. Sys., Meas., Control 139(11), 111006 (Jul 20, 2017) (7 pages) Paper No: DS-16-1608; doi: 10.1115/1.4036560 History: Received December 21, 2016; Revised March 31, 2017

Robots are being increasingly used by physical therapists to carry out rehabilitation treatments owing to their ability of providing repetitive, controlled, and autonomous training sessions. Enhanced treatment outcomes can be achieved by encouraging patients' active participation besides robotic assistance. Advanced control strategies are required to be designed and implemented for the rehabilitation robots in order to persuade patients to contribute actively during the treatments. In this paper, an adaptive impedance control approach is developed and implemented on a parallel ankle rehabilitation robot. The ankle robot was designed based on a parallel mechanism and actuated using four pneumatic muscle actuators (PMAs) to provide three rotational degrees-of-freedom (DOFs) to the ankle joint. The proposed controller adapts the parallel robot's impedance according to the patients' active participation to provide customized robotic assistance. In order to evaluate performance of the proposed controller, experiments were conducted with stroke patients. It is demonstrated from the experimental results that the robotic assistance decreases as a result of patients' active participation. Similarly, increased robotics assistance is recorded in response to decrease in patient's participation in the rehabilitation process. This work will aid in the further development of customized robot-assisted physical therapy of ankle joint impairment.

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Figures

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Fig. 1

Parallel ankle rehabilitation robot

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Fig. 2

Adaptive impedance control architecture implemented in task space. Position controller works on the basis of boundary layer augmented sliding mode control (BASMC) law. The adaptation law (4) modifies the robotic assistance according to the extent of subjects' active participation.

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Fig. 3

Average ankle joint displacement trajectories obtained during inactive mode (i.e., trajectory tracking control mode), averaged over three patients

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Fig. 4

Average ankle joint displacement trajectories obtained during active mode (i.e., zero impedance control mode), averaged over three patients

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Fig. 5

Robot-applied torque at the ankle joint of patients during adaptive impedance control experiments for inactive to active condition, averaged over three patients for two cycles of 30 s each (cycle 1 begins at 0 s and ends at 30 s whereas cycle 2 begins at 30 s and ends at 60 s). The subjects remained inactive (i.e., passive) during cycle 1. At the end of cycle 1, the subjects participated actively in the training process during cycle 2.

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Fig. 6

Robot applied torque at the ankle joint of patients during adaptive impedance control experiments for active to inactive condition, averaged over three patients for two cycles of 30 s each (cycle 1 begins at 0 s and ends at 30 s whereas cycle 2 begins at 30 s and ends at 60 s). The subjects were active during cycle 1. At the end of cycle 1, the subjects remained inactive (i.e., passive) in the training process during cycle 2.

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