scholarly journals Design and Control of a Lower Limb Exoskeleton for Robot-Assisted Gait Training

2009 ◽  
Vol 6 (2) ◽  
pp. 229-243 ◽  
Author(s):  
Pieter Beyl ◽  
Michaël Van Damme ◽  
Ronald Van Ham ◽  
Bram Vanderborght ◽  
Dirk Lefeber
Keyword(s):  
Sliding Mode ◽  
Gait Training ◽  
Human Robot Interaction ◽  
Normal Operation ◽  
Artificial Muscles ◽  
Robot Assisted ◽  
Robot Interaction ◽  
Position Errors ◽  
Pneumatic Artificial Muscles ◽  
And Control

Robot-assisted rehabilitation of gait still faces many challenges, one of which is improving physical human-robot interaction. The use of pleated pneumatic artificial muscles to power a step rehabilitation robot has the potential to meet this challenge. This paper reports on the development of a gait rehabilitation exoskeleton with a knee joint powered by pleated pneumatic artificial muscles. It is intended as a platform for the evaluation of design and control concepts in view of improved physical human-robot interaction. The design was focused on the optimal dimensioning of the actuator configuration. Safety being the most important prerequisite, a proxy-based sliding mode controller (PSMC) was implemented as it combines accurate tracking during normal operation with a smooth, slow and safe recovery from large position errors. Treadmill walking experiments of a healthy subject wearing the powered exoskeleton show the potential of PSMC as a safe robot-in-charge control strategy for robot-assisted gait training.

Passive and Active Control Strategies of a Leg Rehabilitation Exoskeleton Powered by Pneumatic Artificial Muscles

2017 ◽  
Vol 31 (10) ◽  
pp. 1759021 ◽  
Author(s):  
Chen Su ◽  
Ao Chai ◽  
Xikai Tu ◽  
Hongyu Zhou ◽  
Haiqiang Wang ◽  
...  
Keyword(s):  
Lower Limb ◽  
Sliding Mode ◽  
Control Strategies ◽  
Low Cost ◽  
Gait Training ◽  
Interaction Force ◽  
Gait Rehabilitation ◽  
Artificial Muscles ◽  
Model Free ◽  
Pneumatic Artificial Muscles

Nerve injury can cause lower limb paralysis and gait disorder. Currently lower limb rehabilitation exoskeleton robots used in the hospitals need more power to correct abnormal motor patterns of stroke patients’ legs. These gait rehabilitation robots are powered by cumbersome and bulky electric motors, which provides a poor user experience. A newly developed gait rehabilitation exoskeleton robot actuated by low-cost and lightweight pneumatic artificial muscles (PAMs) is presented in this research. A model-free proxy-based sliding mode control (PSMC) strategy and a model-based chattering mitigation robust variable control (CRVC) strategy were developed and first applied in rehabilitation trainings, respectively. As the dynamic response of PAM due to the compressed air is low, an innovative intention identification control strategy was taken in active trainings by the use of the subject’s intention indirectly through the estimation of the interaction force between the subject’s leg and the exoskeleton. The proposed intention identification strategy was verified by treadmill-based gait training experiments.

scholarly journals An extended state observer-based full-order sliding mode control for robotic joint actuated by antagonistic pneumatic artificial muscles

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142098603
Author(s):  
Daoxiong Gong ◽  
Mengyao Pei ◽  
Rui He ◽  
Jianjun Yu
Keyword(s):  
Sliding Mode Control ◽  
Sliding Mode ◽  
State Observer ◽  
Extended State Observer ◽  
Artificial Muscles ◽  
Extended State ◽  
Robot System ◽  
Mode Control ◽  
Physical Experiments ◽  
Pneumatic Artificial Muscles

Pneumatic artificial muscles (PAMs) are expected to play an important role in endowing the advanced robot with the compliant manipulation, which is very important for a robot to coexist and cooperate with humans. However, the strong nonlinear characteristics of PAMs hinder its wide application in robots, and therefore, advanced control algorithms are urgently needed for making the best use of the advantages and bypassing the disadvantages of PAMs. In this article, we propose a full-order sliding mode control extended state observer (fSMC-ESO) algorithm that combines the ESO and the fSMC for a robotic joint actuated by a pair of antagonistic PAMs. The fSMC is employed to eliminate the chattering and to guarantee the finite-time convergence, and the ESO is adopted to observe both the total disturbance and the states of the robot system, so that we can inhibit the disturbance and compensate the nonlinearity efficiently. Both simulations and physical experiments are conducted to validate the proposed method. We suggest that the proposed method can be applied to the robotic systems actuated by PAMs and remarkably improve the performance of the robot system.

scholarly journals A Taxonomy in Robot-Assisted Training: Current Trends, Needs and Challenges

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 119 ◽  
Author(s):  
Konstantinos Tsiakas ◽  
Maria Kyrarini ◽  
Vangelis Karkaletsis ◽  
Fillia Makedon ◽  
Oliver Korn
Keyword(s):  
Training Session ◽  
Training System ◽  
Human Robot Interaction ◽  
Design Development ◽  
Robot Assisted ◽  
Robot Interaction ◽  
Training Systems ◽  
Robot Perception ◽  
Educational Environments ◽  
And Behavior

In this article, we present a taxonomy in Robot-Assisted Training; a growing body of research in Human–Robot Interaction which focuses on how robotic agents and devices can be used to enhance user’s performance during a cognitive or physical training task. Robot-Assisted Training systems have been successfully deployed to enhance the effects of a training session in various contexts, i.e., rehabilitation systems, educational environments, vocational settings, etc. The proposed taxonomy suggests a set of categories and parameters that can be used to characterize such systems, considering the current research trends and needs for the design, development and evaluation of Robot-Assisted Training systems. To this end, we review recent works and applications in Robot-Assisted Training systems, as well as related taxonomies in Human–Robot Interaction. The goal is to identify and discuss open challenges, highlighting the different aspects of a Robot-Assisted Training system, considering both robot perception and behavior control.

scholarly journals Powered Upper Limb Orthosis Actuation System Based on Pneumatic Artificial Muscles

2018 ◽  
Vol 48 (1) ◽  
pp. 23-36 ◽  
Author(s):  
Dimitar Chakarov ◽  
Ivanka Veneva ◽  
Mihail Tsveov ◽  
Pavel Venev
Keyword(s):  
Upper Limb ◽  
Joint Stiffness ◽  
Artificial Muscles ◽  
Joint Motion ◽  
Robot Interaction ◽  
Pneumatic Actuators ◽  
Joint Torques ◽  
Actuation System ◽  
Pneumatic Muscles ◽  
Pneumatic Artificial Muscles

AbstractThe actuation system of a powered upper limb orthosis is studied in the work. To create natural safety in the mutual “man-robot” interaction, an actuation system based on pneumatic artificial muscles (PAM) is selected. Experimentally obtained force/contraction diagrams for bundles, consisting of different number of muscles are shown in the paper. The pooling force and the stiffness of the pneumatic actuators is assessed as a function of the number of muscles in the bundle and the supply pressure. Joint motion and torque is achieved by antagonistic actions through pulleys, driven by bundles of pneumatic muscles. Joint stiffness and joint torques are determined on condition of a power balance, as a function of the joint position, pressure, number of muscles and muscles

scholarly journals Flatness-Based Control of a Two Degrees-of-Freedom Platform With Pneumatic Artificial Muscles

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
David Bou Saba ◽  
Paolo Massioni ◽  
Eric Bideaux ◽  
Xavier Brun
Keyword(s):  
Degrees Of Freedom ◽  
Control Method ◽  
Sliding Mode ◽  
Feedforward Control ◽  
Volume Ratio ◽  
Open Loop ◽  
Control Technique ◽  
Artificial Muscles ◽  
Two Degrees Of Freedom ◽  
Pneumatic Artificial Muscles

Pneumatic artificial muscles (PAMs) are an interesting type of actuators as they provide high power-to-weight and power-to-volume ratio. However, their efficient use requires very accurate control methods taking into account their complex and nonlinear dynamics. This paper considers a two degrees-of-freedom platform whose attitude is determined by three pneumatic muscles controlled by servovalves. An overactuation is present as three muscles are controlled for only two degrees-of-freedom. The contribution of this work is twofold. First, whereas most of the literature approaches the control of systems of similar nature with sliding mode control, we show that the platform can be controlled with the flatness-based approach. This method is a nonlinear open-loop controller. In addition, this approach is model-based, and it can be applied thanks to the accurate models of the muscles, the platform and the servovalves, experimentally developed. In addition to the flatness-based controller, which is mainly a feedforward control, a proportional-integral (PI) controller is added in order to overcome the modeling errors and to improve the control robustness. Second, we solve the overactuation of the platform by an adequate choice for the range of the efforts applied by the muscles. In this paper, we recall the basics of this control technique and then show how it is applied to the proposed experimental platform. At the end of the paper, the proposed approach is compared to the most commonly used control method, and its effectiveness is shown by means of experimental results.

scholarly journals A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Yiming Jiang ◽  
Chenguang Yang ◽  
Jing Na ◽  
Guang Li ◽  
Yanan Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Human Robot Interaction ◽  
Learning Tools ◽  
Robot Interaction ◽  
Practical Applications ◽  
Powerful Learning ◽  
Wide Range ◽  
Complex Nonlinear Systems ◽  
And Control

As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control.

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