Adaptive impedance control for upper limb assist exoskeleton

Khan, Abdul Manan; Yun, Doo-Hee; Ali, Mian Ashfaq; Han, Jung‐Soo; Shin, Kyung-Hoon; Han, Changsoo

doi:10.1109/icra.2015.7139801

Cited by 33 publications

(18 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, their implementation strongly depends on the developed robotic systems. For example, the Harmony exoskeleton exploits an explicit SEA-based impedance controller (Kim et al, 2017 ), which is similar to our approach, while other exoskeletons, such as ARMin, use instead implicit impedance controllers to promote rehabilitation exercises (Nef et al, 2007 ; Khan et al, 2015 ). However, the generalization of these approaches to a large variety of human-robot interaction modalities, their integration in a unified low-level compliant controller, and the validation of the perceived pHRI through the assessment of the voluntary human effort have not been investigated yet.…”

Section: Discussionmentioning

confidence: 99%

Development and Electromyographic Validation of a Compliant Human-Robot Interaction Controller for Cooperative and Personalized Neurorehabilitation

et al. 2022

View full text Add to dashboard Cite

BackgroundAppropriate training modalities for post-stroke upper-limb rehabilitation are key features for effective recovery after the acute event. This study presents a cooperative control framework that promotes compliant motion and implements a variety of high-level rehabilitation modalities with a unified low-level explicit impedance control law. The core idea is that we can change the haptic behavior perceived by a human when interacting with the rehabilitation robot by tuning three impedance control parameters.MethodsThe presented control law is based on an impedance controller with direct torque measurement, provided with positive-feedback compensation terms for disturbances rejection and gravity compensation. We developed an elbow flexion-extension experimental setup as a platform to validate the performance of the proposed controller to promote the desired high-level behavior. The controller was first characterized through experimental trials regarding joint transparency, torque, and impedance tracking accuracy. Then, to validate if the controller could effectively render different physical human-robot interaction according to the selected rehabilitation modalities, we conducted tests on 14 healthy volunteers and measured their muscular voluntary effort through surface electromyography (sEMG). The experiments consisted of one degree-of-freedom elbow flexion/extension movements, executed under six high-level modalities, characterized by different levels of (i) corrective assistance, (ii) weight counterbalance assistance, and (iii) resistance.ResultsThe unified controller demonstrated suitability to promote good transparency and render both compliant and stiff behavior at the joint. We demonstrated through electromyographic monitoring that a proper combination of stiffness, damping, and weight assistance could induce different user participation levels, render different physical human-robot interaction, and potentially promote different rehabilitation training modalities.ConclusionWe proved that the proposed control framework could render a wide variety of physical human-robot interaction, helping the user to accomplish the task while exploiting physiological muscular activation patterns. The reported results confirmed that the control scheme could induce different levels of the subject's participation, potentially applicable to the clinical practice to adapt the rehabilitation treatment to the subject's progress. Further investigation is needed to validate the presented approach to neurological patients.

show abstract

Section: Discussionmentioning

confidence: 99%

Development and Electromyographic Validation of a Compliant Human-Robot Interaction Controller for Cooperative and Personalized Neurorehabilitation

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Applying such methods in clinical situations is therefore not straightforward, particularly for early stages of recovery. In this context, a number of adaptive impedance approaches have been developed to promote and maintain patient participation [41], [42]. Pérez-Ibarra et al [43] proposed an assistive-resistive strategy, which can determine appropriate task difficulty by estimating the dynamic contribution of the patient based on an exoskeleton dynamic model.…”

Section: B Related Workmentioning

confidence: 99%

Model Predictive Control for Human-Centred Lower Limb Robotic Assistance

Caulcrick¹,

Huo²,

Franco³

et al. 2020

Preprint

View full text Add to dashboard Cite

Loss of mobility or balance resulting from neural trauma is a critical consideration in public health. Robotic exoskeletons hold great potential for rehabilitation and assisted movement, yet optimal assist-as-needed (AAN) control remains unresolved given pathological variance among patients. We introduce a model predictive control (MPC) architecture for lower limb exoskeletons centred around a fuzzy logic algorithm (FLA) identifying modes of assistance based on human involvement. Assistance modes are: 1) passive for human relaxed and robot dominant, 2) active-assist for human cooperation with the task, and 3) safety in the case of human resistance to the robot. Human torque is estimated from electromyography (EMG) signals prior to joint motions, enabling advanced prediction of torque by the MPC and selection of assistance mode by the FLA. The controller is demonstrated in hardware with three subjects on a 1-DOF knee exoskeleton tracking a sinusoidal trajectory with human relaxed, assistive, and resistive. Experimental results show quick and appropriate transfers among the assistance modes and satisfied assistive performance in each mode. Results illustrate an objective approach to lower limb robotic assistance through on-the-fly transition between modes of movement, providing a new level of human-robot synergy for mobility assist and rehabilitation.

show abstract

“…Huang and Chien [ 31 ] used regressor-free adaptive backstepping control for flexible joints; they used ( 8 ) as the target impedance dynamics. Khan et al [ 46 ] used adaptive impedance control based on the target impedance dynamics of ( 8 ) for an upper limb assist exoskeleton. Please see [ 7 , 47 , 48 ] for more details on the characteristics and limitations of this impedance control scheme.…”

Section: Introductionmentioning

confidence: 99%

Active Impedance Control of Bioinspired Motion Robotic Manipulators: An Overview

Al-Shuka

Leonhardt

Zhu

et al. 2018

Applied Bionics and Biomechanics

View full text Add to dashboard Cite

There are two main categories of force control schemes: hybrid position-force control and impedance control. However, the former does not take into account the dynamic interaction between the robot's end effector and the environment. In contrast, impedance control includes regulation and stabilization of robot motion by creating a mathematical relationship between the interaction forces and the reference trajectories. It involves an energetic pair of a flow and an effort, instead of controlling a single position or a force. A mass-spring-damper impedance filter is generally used for safe interaction purposes. Tuning the parameters of the impedance filter is important and, if an unsuitable strategy is used, this can lead to unstable contact. Humans, however, have exceptionally effective control systems with advanced biological actuators. An individual can manipulate muscle stiffness to comply with the interaction forces. Accordingly, the parameters of the impedance filter should be time varying rather than value constant in order to match human behavior during interaction tasks. Therefore, this paper presents an overview of impedance control strategies including standard and extended control schemes. Standard controllers cover impedance and admittance architectures. Extended control schemes include admittance control with force tracking, variable impedance control, and impedance control of flexible joints. The categories of impedance control and their features and limitations are well introduced. Attention is paid to variable impedance control while considering the possible control schemes, the performance, stability, and the integration of constant compliant elements with the host robot.

show abstract

Adaptive impedance control for upper limb assist exoskeleton

Cited by 33 publications

References 20 publications

Development and Electromyographic Validation of a Compliant Human-Robot Interaction Controller for Cooperative and Personalized Neurorehabilitation

Development and Electromyographic Validation of a Compliant Human-Robot Interaction Controller for Cooperative and Personalized Neurorehabilitation

Model Predictive Control for Human-Centred Lower Limb Robotic Assistance

Active Impedance Control of Bioinspired Motion Robotic Manipulators: An Overview

Contact Info

Product

Resources

About