Feedback Control Design for Robust Comfortable Sit-to-Stand Motions of 3D Lower-Limb Exoskeletons

Mungai, M. Eva; Grizzle, Jessy W.

doi:10.1109/access.2020.3046446

Cited by 12 publications

(8 citation statements)

References 62 publications

(99 reference statements)

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“…To assist the user with the exoskeleton robot, predesigned policies, such as guiding the user in a predefined trajectory [8] were often used to control and trigger-based interventions are natural if the user only performs the expected motion [9].…”

Section: Related Workmentioning

confidence: 99%

“…The conventional studies on standing up motion assist have focused only on the transition from sitting (negative label) to standing-up (positive label) and assumed a situation in which the state transitions occur only between them. Therefore, they often controlled the robot based on the triggers for the transition from sitting to standing up [9], [10]. The STS-PN, which determines the trigger by data-driven, assumes the same transition, and we consider it as the conventional approach.…”

Section: Evaluation Of the Proposed Approachmentioning

confidence: 99%

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Selective Assist Strategy by Using Lightweight Carbon Frame Exoskeleton Robot

Furukawa¹,

Okajima²,

et al. 2022

IEEE Robot. Autom. Lett.

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Exoskeleton robots need to always actively assist the user's movements otherwise robot just becomes a heavy load for the user. However, estimating diversified movement intentions in a user's daily life is not easy and no algorithm so far has achieved that level of estimation. In this study, we rather focus on estimating and assisting a limited number of selected movements by using an EMG-based movement classification and a newly developed lightweight exoskeleton robot. Our lightweight knee exoskeleton is composed of a carbon fiber frame and highly backdrivable joint driven by a pneumatic artificial muscle. Thus, our robot does not interfere with the user's motions even when the actuator is not activated. As the classification method, we adopted a positive-unlabeled (PU) classifier. Since precisely labeling all the selected data from large-scale daily movements is not practical, we assumed that only part of the selected data was labeled and used a PU classifier that can handle the unlabeled data. To validate our approach, we conducted experiments with five healthy subjects to selectively assist sit-tostand movements from four possible daily motions. We compared our approach with two classification methods that assume fully labeled data. The results showed that all subject's movements were properly assisted.

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Section: Related Workmentioning

confidence: 99%

Section: Evaluation Of the Proposed Approachmentioning

confidence: 99%

Selective Assist Strategy by Using Lightweight Carbon Frame Exoskeleton Robot

Furukawa¹,

Okajima²,

et al. 2022

IEEE Robot. Autom. Lett.

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“…Mungai et al [188] People with paraplegia / simulation only hip f/e a/a r/c; knee f/e; ankle d/p a/a / all joint angle and velocity; ground and chair contact force quadratic programming and input/output linearization was combined to compute the optimal torque output for overactuated system rendered by numerous contact constraint in the sit-to-stand process. by Vouge et al [130] that used continuous regression to enable a monkey wearing an exoskeleton to track a cursor on a screen.…”

Section: ) Sensing Technologiesmentioning

confidence: 99%

Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons

Cao²,

Zhu

2021

IEEE Access

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Research on lower limb exoskeleton (LLE) for rehabilitation have developed rapidly to meet the need of the population with neurologic injuries. LLEs for rehabilitation include therapeutic LLEs that aim to restore walking ability for patients, and assistive LLEs that offer support on activities in daily life. A substantial part of them can serve both purposes. However, these devices are yet to reach the final goal of performing human-machine joint movement agilely and smartly. Control strategy plays an important role in achieving their designed goal. At present, control strategies face three major challenges: how to detect human intention, how to do motion control with given intentions, and how to optimize control parameters to suit different individuals. As a contribution, this paper offers an overview on the state-of-the-art control strategies for rehabilitation LLEs by classifying them into eight categories, each of which is presented with a technical summary and tabulated information of representative papers. Moreover, current approaches addressing the three challenges are discussed in a macroscopic perspective. Finally, it has been explored which requirements the future control strategies should meet for maximizing the performance of rehabilitation LLEs.

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“…Exoskeletons with feet can topple over, and we use an inequality-constrained floating base model Koolen et al (2016) ; Kim et al (2016 , 2018) ; Mungai and Grizzle (2020) to keeping its feet flat on the ground. These inequality constraints act on the base–ground reaction forces, f r defined by the combination of the floating-base exoskeleton and a contact constraint (notation as in Table 4 ):

…”

Section: Weighted 1-norm Shared-body Controlmentioning

confidence: 99%

Formulating and Deploying Strength Amplification Controllers for Lower-Body Walking Exoskeletons

et al. 2021

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Augmenting the physical strength of a human operator during unpredictable human-directed (volitional) movements is a relevant capability for several proposed exoskeleton applications, including mobility augmentation, manual material handling, and tool operation. Unlike controllers and augmentation systems designed for repetitive tasks (e.g., walking), we approach physical strength augmentation by a task-agnostic method of force amplification—using force/torque sensors at the human–machine interface to estimate the human task force, and then amplifying it with the exoskeleton. We deploy an amplification controller that is integrated into a complete whole-body control framework for controlling exoskeletons that includes human-led foot transitions, inequality constraints, and a computationally efficient prioritization. A powered lower-body exoskeleton is used to demonstrate behavior of the control framework in a lab environment. This exoskeleton can assist the operator in lifting an unknown backpack payload while remaining fully backdrivable.

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Feedback Control Design for Robust Comfortable Sit-to-Stand Motions of 3D Lower-Limb Exoskeletons

Cited by 12 publications

References 62 publications

Selective Assist Strategy by Using Lightweight Carbon Frame Exoskeleton Robot

Selective Assist Strategy by Using Lightweight Carbon Frame Exoskeleton Robot

Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons

Formulating and Deploying Strength Amplification Controllers for Lower-Body Walking Exoskeletons

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