Modeling and Stiffness-Based Continuous Torque Control of Lightweight Quasi-Direct-Drive Knee Exoskeletons for Versatile Walking Assistance

Huang, Tzu-Hao; Zhang, Sainan; Yu, Shuangyue; MacLean, Mhairi K.; Zhu, Junxi; Lallo, Antonio Di; Jiao, Chunhai; Bulea, Thomas C.; Zheng, Minghui; Su, Hao

doi:10.1109/tro.2022.3170287

Cited by 37 publications

(21 citation statements)

References 66 publications

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“…As the approach requires lightweight and compact actuators in exoskeleton systems and the performance demands in various external force situations encountered during activities of daily living (ADL), direct drive methods were increasingly adopted in various exoskeleton systems. [27,28] Utilizing these principles, the system implemented a quasi-direct drive actuator by connecting a commercial BLDC motor with the appropriate gear ratio to a 3D-printed planetary gear produced with rapid prototyping.…”

Section: Design Principles and Key Assembly Considerations Of A Metav...mentioning

confidence: 99%

A Hybrid Upper‐Arm‐Geared Exoskeleton with Anatomical Digital Twin for Tangible Metaverse Feedback and Communication

Ha,

Lee,

Cho

et al. 2023

Adv Materials Technologies

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The pandemic coincided with rapid advancements in virtual reality (VR) and mixed reality (MR) in healthcare. The idea of virtually replicating the real world and its associated experiences has garnered significant attention under the newly coined term “metaverse.” The metaverse serves as a communication platform that integrates physical and virtual experiences. However, the lack of physical interaction between users and virtual environments remains an obstacle. This study introduces a hybrid upper‐arm‐geared exoskeleton system that combines an anatomical digital‐twin model with tangible VR torque feedback and an MR remote healthcare monitoring in a musculoskeletal interface device format. The device employs a dual epicyclic geared motor actuator capable of exerting torque feedback. It derived torque profiles that simulate physical interactions with hysteretic damping in the virtual environment. An anatomical digital twin model is also incorporated into the fabricated device in an MR format and assessed the device's performance using electromyogram and thermographic sensors. In addition, it evaluated haptic fidelity and versatility as an immersive metaverse wearable and performed support vector machine‐based analysis for motion feature classification. It believes this approach has the potential to be highly beneficial for providing tangible metaverse feedback and communication combined with a human‐informed digital‐twin model for remote healthcare monitoring in the postpandemic era.

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Section: Design Principles and Key Assembly Considerations Of A Metav...mentioning

confidence: 99%

A Hybrid Upper‐Arm‐Geared Exoskeleton with Anatomical Digital Twin for Tangible Metaverse Feedback and Communication

Ha,

Lee,

Cho

et al. 2023

Adv Materials Technologies

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“…Thatte et al [ 15 ] developed a continuous torque curve to assist knee and ankle prostheses by utilizing the joint angle, angular velocity, and feedforward torque. Huang et al [ 16 ] conducted stiffness modeling for a lightweight quasi-direct-drive (QDD) knee exoskeleton robot and carried out continuous torque control based on stiffness, achieving a larger stiffness tracking bandwidth and a smaller torque tracking error, but their assist torque curve was only based on the proportional biological torque curve. Bryan et al [ 17 ] studied the relationship between walking speed, exoskeleton power, and metabolic energy expenditure and concluded that the exoskeleton robot is most effective during moderate and fast walking and less effective during slow walking.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Torque-Control Model for Quasi-Direct-Drive Knee Exoskeleton Robots Based on Regression Forecasting

Xia,

Wei,

Lin

et al. 2024

Sensors

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The choice of torque curve in lower-limb enhanced exoskeleton robots is a key problem in the control of lower-limb exoskeleton robots. As a human–machine coupled system, mapping from sensor data to joint torque is complex and non-linear, making it difficult to accurately model using mathematical tools. In this research study, the knee torque data of an exoskeleton robot climbing up stairs were obtained using an optical motion-capture system and three-dimensional force-measuring tables, and the inertial measurement unit (IMU) data of the lower limbs of the exoskeleton robot were simultaneously collected. Nonlinear approximations can be learned using machine learning methods. In this research study, a multivariate network model combining CNN and LSTM was used for nonlinear regression forecasting, and a knee joint torque-control model was obtained. Due to delays in mechanical transmission, communication, and the bottom controller, the actual torque curve will lag behind the theoretical curve. In order to compensate for these delays, different time shifts of the torque curve were carried out in the model-training stage to produce different control models. The above model was applied to a lightweight knee exoskeleton robot. The performance of the exoskeleton robot was evaluated using surface electromyography (sEMG) experiments, and the effects of different time-shifting parameters on the performance were compared. During testing, the sEMG activity of the rectus femoris (RF) decreased by 20.87%, while the sEMG activity of the vastus medialis (VM) increased by 17.45%. The experimental results verify the effectiveness of this control model in assisting knee joints in climbing up stairs.

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“…The advantages of our controller are due to its nonlinearity and multi-levels that allow spatial learning capability of joint-angle-related uncertainties and disturbances. This paper has a different scope and contributions compared to the prior work of our group [2] which only focuses on exoskeleton mechatronics and high-level control design, while this paper focuses on developing a multi-level controller by involving a mid-level controller (which provides a trajectory for the exoskeleton to follow) and a low-level controller (which helps the human user achieve the reference assistive torque by trajectory tracking) for desired torque assistance. It also differs from [22], which may need parameter estimation for disturbance learning.…”

Section: Introductionmentioning

confidence: 99%

Spatial Iterative Learning Torque Control of Robotic Exoskeletons for High Accuracy and Rapid Convergence Assistance

Xing,

Zhang,

Huang

et al. 2024

IEEE/ASME Trans. Mechatron.

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High-performance torque tracking is crucial for accurate control of the magnitude and timing of exoskeleton assistive torque profiles. However, state-of-the-art torque control methods, e.g., iterative learning control (ILC), applied to exoskeletons cannot achieve satisfying accuracy and convergence speed. This paper aims to design a spatial iterative learning (sIL)-based torque control strategy for exoskeletons to achieve accurate and fast torque assistance, which includes a high-level controller for torque planning, a mid-level one for reference trajectory generation, and a low-level one for trajectory tracking. Compared with ILC, our proposed sIL-based control method can estimate and compensate for spatial uncertainties (e.g., joint-angle-related uncertain dynamics of the human-exoskeleton interaction system) and spatial disturbances (e.g., joint-anglerelated disturbances caused by physical interaction with the human limb) that commonly exist in exoskeletons for highly accurate torque assistance. Furthermore, our control can ensure accurate torque tracking during unsteady-state gaits with fast convergence thanks to its spatial learning capability that enables varying iterative learning speeds to deal with varying walking speeds of users for different iterations, which is not feasible by ILC methods. Experiments showed that compared with the state-of-the-art torque control methods, our sIL-based control method significantly improved the torque tracking accuracy and shortened the convergence time for both steady-state walking and unsteady-state walking (with sudden or gradual changes in gait speeds), which demonstrates its effectiveness.

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Modeling and Stiffness-Based Continuous Torque Control of Lightweight Quasi-Direct-Drive Knee Exoskeletons for Versatile Walking Assistance

Cited by 37 publications

References 66 publications

A Hybrid Upper‐Arm‐Geared Exoskeleton with Anatomical Digital Twin for Tangible Metaverse Feedback and Communication

A Hybrid Upper‐Arm‐Geared Exoskeleton with Anatomical Digital Twin for Tangible Metaverse Feedback and Communication

Optimization of Torque-Control Model for Quasi-Direct-Drive Knee Exoskeleton Robots Based on Regression Forecasting

Spatial Iterative Learning Torque Control of Robotic Exoskeletons for High Accuracy and Rapid Convergence Assistance

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