Modeling and control of an active knee orthosis using a computational model of the musculoskeletal system

2018 ◽  
Vol 1 (3) ◽  
pp. 12
Author(s):  
Denis Mosconi ◽  
Polyana Ferreira Nunes ◽  
Adriano Almeida Gonçalves Siqueira
Keyword(s):  
Computational Model ◽  
Inverse Dynamics ◽  
Human Robot Interaction ◽  
Robotic Rehabilitation ◽  
Robot Interaction ◽  
Modeling And Control ◽  
Interaction Control ◽  
Knee Movement ◽  
Feedforward Controller ◽  
And Control

One-third of the stroke survivors remain with some disability, needing assistance to perform the activities of daily life and therapy to recover the lost functions.  The robotic rehabilitation is a promissed field in this context improving the effectiveness of the treatment. Many researches have focused on developing human-robot interaction control to ensure user safety and therapy efficiency, but the validation of these controllers often requires contact between humans and robots, which involves cost, time and risk of accidents. This work aims to present a computational model of an ideal active orthosis used to assist the knee movement as a tool for test and validate human-robot interaction controls. Three controllers were applied to make the orthosis move the knee tracking the desired trajectory: a PID controller, an Inverse Dynamics-Based controller, and a Feedback-Feedforward Controller. The model proved to be useful and the controller with the best performance was the Feedback-Feedforward one.

scholarly journals Systematic framework for performance evaluation of exoskeleton actuators

2020 ◽  
Vol 1 ◽  
Author(s):  
Christian Di Natali ◽  
Stefano Toxiri ◽  
Stefanos Ioakeimidis ◽  
Darwin G. Caldwell ◽  
Jesús Ortiz
Keyword(s):  
Evaluation Method ◽  
Inverse Dynamics ◽  
Dynamic Performance ◽  
Human Robot Interaction ◽  
Control Algorithms ◽  
Kinematics And Dynamics ◽  
Actuation System ◽  
And Control ◽  
Systematic Framework ◽  
Actuator Performance

Abstract Wearable devices, such as exoskeletons, are becoming increasingly common and are being used mainly for improving motility and daily life autonomy, rehabilitation purposes, and as industrial aids. There are many variables that must be optimized to create an efficient, smoothly operating device. The selection of a suitable actuator is one of these variables, and the actuators are usually sized after studying the kinematic and dynamic characteristics of the target task, combining information from motion tracking, inverse dynamics, and force plates. While this may be a good method for approximate sizing of actuators, a more detailed approach is necessary to fully understand actuator performance, control algorithms or sensing strategies, and their impact on weight, dynamic performance, energy consumption, complexity, and cost. This work describes a learning-based evaluation method to provide this more detailed analysis of an actuation system for our XoTrunk exoskeleton. The study includes: (a) a real-world experimental setup to gather kinematics and dynamics data; (b) simulation of the actuation system focusing on motor performance and control strategy; (c) experimental validation of the simulation; and (d) testing in real scenarios. This study creates a systematic framework to analyze actuator performance and control algorithms to improve operation in the real scenario by replicating the kinematics and dynamics of the human–robot interaction. Implementation of this approach shows substantial improvement in the task-related performance when applied on a back-support exoskeleton during a walking task.

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