Dynamic Modeling and Motion Control of a Cable-Driven Robotic Exoskeleton With Pneumatic Artificial Muscle Actuators

Chen, Chun Ta; Lien, Wei Yuan; Twu, Ming-Jenq; Wu, Yu Cheng

doi:10.1109/access.2020.3016726

Cited by 31 publications

(36 citation statements)

References 39 publications

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“…In both cases, the logic and activation support were carried out in an ergonomic backpack and not directly on the upper limb. Other developments that reported cable-based actuators [ 49 ] do not have the same mechanical qualities due to their larger structures.…”

Section: Resultsmentioning

confidence: 99%

“…Some of the developments shown in this literature review corresponded to rigid bilateral systems [ 27 , 34 , 49 , 55 ], designed specifically for synchronous motor rehabilitation. Some of them proposed different methodologies to detect the movement intentions and to replicate them using a leader–follower architecture.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, the development shown in [ 49 ] has provided different modes of operation, such as patient-active-robot-passive and patient-passive-robot-active training, achieved based on trajectory tracking control.…”

Section: Resultsmentioning

confidence: 99%

“…An article has presented the implementation of four DoFs [ 49 ]. This system is driven by pneumatic elements, and is characterised by a safe, compact, and light structure that complies with the movement of an upper limb as closely as possible to reality.…”

Section: Resultsmentioning

confidence: 99%

“…“Research efforts were also concentrated on the dynamic modelling and controller design for the reliable implementation of rehabilitation exercises. The dynamic model was developed in terms of quasi coordinates combined with the virtual work principle” [ 49 ].…”

Section: Resultsmentioning

confidence: 99%

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Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Vélez-Guerrero

Callejas-Cuervo

Mazzoleni

2021

Sensors

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Processing and control systems based on artificial intelligence (AI) have progressively improved mobile robotic exoskeletons used in upper-limb motor rehabilitation. This systematic review presents the advances and trends of those technologies. A literature search was performed in Scopus, IEEE Xplore, Web of Science, and PubMed using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology with three main inclusion criteria: (a) motor or neuromotor rehabilitation for upper limbs, (b) mobile robotic exoskeletons, and (c) AI. The period under investigation spanned from 2016 to 2020, resulting in 30 articles that met the criteria. The literature showed the use of artificial neural networks (40%), adaptive algorithms (20%), and other mixed AI techniques (40%). Additionally, it was found that in only 16% of the articles, developments focused on neuromotor rehabilitation. The main trend in the research is the development of wearable robotic exoskeletons (53%) and the fusion of data collected from multiple sensors that enrich the training of intelligent algorithms. There is a latent need to develop more reliable systems through clinical validation and improvement of technical characteristics, such as weight/dimensions of devices, in order to have positive impacts on the rehabilitation process and improve the interactions among patients, teams of health professionals, and technology.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Vélez-Guerrero

Callejas-Cuervo

Mazzoleni

2021

Sensors

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Control Method of Upper Limb Rehabilitation Exoskeleton for Better Assistance: A Comprehensive Review

Wang,

Ren,

et al. 2024

Journal of Field Robotics

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The upper limb rehabilitation exoskeleton is a robotic‐arm‐like device that fits the human upper limb and assists in movement, having the potential to be widely used in medical practice. The control method of the upper limb rehabilitation exoskeleton system is an important factor that affects the effectiveness of its rehabilitation training assistance and is also the focus of research in this field. In this article, we divide the control method of the upper limb rehabilitation exoskeleton into two levels, the high‐level control mode (including passive mode, active mode, and ANN, etc.) and the low‐level controller. The design of the controller aims to meet the requirements of the control mode but faces difficulties such as complex dynamic models of the system, unknown external disturbances, and motion intention recognition to achieve accurate motion trajectory tracking and flexible human–robot interaction. Based on relevant literature in the field of upper limb rehabilitation exoskeleton control methods in recent years, we analyze the rehabilitation training control modes that researchers aim to achieve, as well as the work they have done in controller design to achieve these control modes. We also propose potential research directions for achieving better exoskeleton‐assisted training effects.

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Ultrafast, High‐Contractile Electrothermal‐Driven Liquid Crystal Elastomer Fibers towards Artificial Muscles

Sun

Wang

Liao

et al. 2021

Small

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For example, when an LCE fiber is lifting a heavy load, a high contraction ratio produces a larger displacement and thus a higher external work output. A fast contraction rate, on the other hand, shortens the time required to lift the load and increases the work efficiency of the LCE fiber. In nature, human skeletal muscle can produce larger than 40% contraction at a contraction rate of higher than 50% s −1 , [1,18] allowing it to perform intense movements. For example, when humans are doing sports, e.g., basketball shooting, their muscles can contract both substantially and rapidly. However, none of the LCE fibers reported so far can simultaneously deform with a large contraction ratio and fast contraction rate comparable to human muscles, restricting its applications to conduct intense movements. To date, thermal-responsive LCE fibers are capable of generating large deformation. [19][20][21][22] But the heating rate is limited by the low thermal conductivity of air, leading to a slow contraction of LCE. Second, although light is a precise and fast stimulus, it can only propagate along a straight line. Thus, photo-responsive LCE fibers are usually exposed to light only on one side and typically demonstrate bending deformation, bringing about difficulties to achieve substantial contractile deformation. [23][24][25][26] Overall, with the development of LCE fibers, the gap that LCE fibers can output large contraction at an ultrafast rate needs to be filled.Herein, we report electrothermal-responsive liquid metal (LM) containing LCE (LM-LCE) fibers that can export large contraction with an ultrafast speed. Comparing to rigid conductive fillers, the fluidic property of LM alleviates the restriction to the deformation of LCE, [27,28] ensuring a large contraction generated by LCE. As a high temporal resolution stimulus, the electrical trigger (e.g., voltage value and pulse time) can be adjusted to achieve high instantaneous input of electrical energy and rapidly heat the entire LM-LCE fibers, resulting in a high contraction rate. The electrothermal-responsive LM-LCE fibers can be used as artificial muscles to drive soft robots to perform various functions with intense actuation. Result and DiscussionLM-LCE fibers were fabricated as shown in Figure 1a. First, LCE fibers oriented along the long axis were prepared according to Liquid crystal elastomer (LCE) fibers are capable of large and reversible deformations, making them an ideal artificial muscle. However, limited to stimulating source and structural design, current LCE fibers have not yet achieved both large contraction ratio and fast contraction rate to perform the intense motion. In this work, electrothermal-responsive liquid metal (LM) containing LCE (LM-LCE) fibers is reported. By introducing flexible liquid metal, LM-LCE fibers retain deformability with a large contraction ratio similar to that of pure LCE fibers and are endowed with electrical responsiveness. Applying precisely controlled electrical stimulation, the contraction ratio and rate of LM-LCE fibers ...

show abstract

Dynamic Modeling and Motion Control of a Cable-Driven Robotic Exoskeleton With Pneumatic Artificial Muscle Actuators

Cited by 31 publications

References 39 publications

Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review

Control Method of Upper Limb Rehabilitation Exoskeleton for Better Assistance: A Comprehensive Review

Ultrafast, High‐Contractile Electrothermal‐Driven Liquid Crystal Elastomer Fibers towards Artificial Muscles

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