Design and Actuator Selection of a Lower Extremity Exoskeleton

Önen, Ümit; Botsalı, Fatih Mehmet; Kalyoncu, Mete; Tınkır, Mustafa; Yilmaz, Nihat; Şahin, Yusuf

doi:10.1109/tmech.2013.2250295

Cited by 77 publications

(42 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In order to obtain the desired gait trajectory, a simple kinematic model of the lower limb is developed based on the hip angle θ hip and knee angle θ knee obtained from the Clinical Gait Analysis Normative Gait Database [37], which can be formulated as…”

Section: Inverse Kinematic Analysismentioning

confidence: 99%

Conceptual design and dimensional synthesis of cam-linkage mechanisms for gait rehabilitation

Shao

Xiang

Liu

et al. 2016

Mechanism and Machine Theory

View full text Add to dashboard Cite

Section: Inverse Kinematic Analysismentioning

confidence: 99%

Conceptual design and dimensional synthesis of cam-linkage mechanisms for gait rehabilitation

Shao

Xiang

Liu

et al. 2016

Mechanism and Machine Theory

View full text Add to dashboard Cite

“…• Design must be flexible: The length of the thigh, shank and waist should be adjustable [5]. The length variation of thigh and shank is about 6 cm for average people with height from 1.60 m • Increase joint strength: Exoskeletons do not transfer substantial load to the ground, but augments joint torque.…”

Section: Lower Limb Exoskeleton Featuresmentioning

confidence: 99%

Mechatronic Design of a Lower Limb Exoskeleton

Minchala¹,

Astudillo‐Salinas²,

Palacio‐Baus³

et al. 2017

Design, Control and Applications of Mechatronic Systems in Engineering

View full text Add to dashboard Cite

This chapter presents a lower limb exoskeleton mechatronic design. The design aims to be used as a walking support device focused on patients who suffer of partial lower body paralysis due to spine injuries or caused by a stroke. First, the mechanical design is presented and the results are validated through dynamical simulations performed in Autodesk Inventor and MATLAB. Second, a communication network design is proposed in order to establish a secure and fast data link between sensors, actuators, and microprocessors. Finally, patient-exoskeleton system interaction is presented and detailed. Movement generation is performed by means of digital signal processing techniques applied to electromyography (EMG) and electrocardiography (EEG) signals.Such interaction system design is tested and evaluated in MATLAB whose results are presented and explained. A proposal of real-time supervisory control is also presented as a part of the integration of every component of the exoskeleton.

show abstract

“…5. We design the PD controller including a gravity compensator for the stance leg phase as follows: (5) where q h , q e are the angular positions of the human and the exoskeleton limbs respectively;Ĝ st (q e ) is the Fig. 4 The hybrid control scheme of the PRMI exoskeleton system Substituting Eqs.…”

Section: The Pd Control For Stance Leg Phasementioning

confidence: 99%

“…Exoskeletons could be classified by two groups. The first group are exoskeletons for assisting the wearer in the case of a diminished functionality to stand and locomote called assisting exoskeleton [2][3][4][5][6][7][8]. The second group are exoskeletons for augmentation of the able bodied wearer physical abilities called human performance augmenting exoskeleton [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Minimizing Human-Exoskeleton Interaction Force Using Compensation for Dynamic Uncertainty Error with Adaptive RBF Network

Duong

Cheng

Tran

et al. 2015

J Intell Robot Syst

View full text Add to dashboard Cite

A critical issue in the control of exoskeleton systems is unknown nonlinear dynamic properties of the system. The improper estimation of those unknown properties can cause considerable humanexoskeleton interaction force during human's movements. It is really challenging to exactly estimate the parameters of dynamic models. In this paper, we propose a novel exoskeleton control algorithm to both compensate for the dynamic uncertainty error and minimize the human-exoskeleton interaction force. We have built a virtual torque controller based on dynamic models of a lower exoskeleton and have used an approximation of a Radial Basis Function (RBF) neural network to compensate for the dynamic uncertainty error. By doing so, we avoid using complicated force sensors installed on the human-exoskeleton interface and minimize the physical Human-Robot Interaction (pHRI) force. Moreover, we introduce the prototype of our exoskeleton system, called 'PRMI' exoskeleton system. Finally, we validated the proposed algorithm on this system, and the experimental results show that the proposed control algorithm provides a good control quality for the 'PRMI' exoskeleton system by compensating for dynamic uncertainty error.

show abstract

Design and Actuator Selection of a Lower Extremity Exoskeleton

Cited by 77 publications

References 21 publications

Conceptual design and dimensional synthesis of cam-linkage mechanisms for gait rehabilitation

Conceptual design and dimensional synthesis of cam-linkage mechanisms for gait rehabilitation

Mechatronic Design of a Lower Limb Exoskeleton

Minimizing Human-Exoskeleton Interaction Force Using Compensation for Dynamic Uncertainty Error with Adaptive RBF Network

Contact Info

Product

Resources

About