Human-in-the-Loop Control of a Wearable Lower Limb Exoskeleton for Stable Dynamic Walking

Li, Zhijun; Zhao, Kongshuang; Zhang, Longbin; Wang, Xinyu; Zhang, Tao; Li, Qinjian; Li, Xiang; Su, Chun‐Yi

doi:10.1109/tmech.2020.3044289

Cited by 75 publications

(23 citation statements)

References 31 publications

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“…The human subject wearing a robotic assistive device that interacts with the environment forms a closed-loop system with two separate controllers: the human central nervous system (CNS) and the robot control system [ 1 ]. These two control systems simultaneously modify the behavior of the closed-loop system with different approaches of robot’s hierarchical control [ 2 , 3 ] and human’s optimal control [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the robot controller should be appropriately modified as the human adapts to the device to account for human–robot coadaptation. For this purpose, researchers have either conducted human-in-the-loop studies or personalized computer simulations [ 1 , 6 , 7 , 8 ]. Since safety is a major consideration in biomechatronics research, this paper adopts the latter approach; the results of this research pave the way for future experimental implementations.…”

Section: Introductionmentioning

confidence: 99%

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Model-Based Mid-Level Regulation for Assist-As-Needed Hierarchical Control of Wearable Robots: A Computational Study of Human-Robot Adaptation

2022

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The closed-loop human–robot system requires developing an effective robotic controller that considers models of both the human and the robot, as well as human adaptation to the robot. This paper develops a mid-level controller providing assist-as-needed (AAN) policies in a hierarchical control setting using two novel methods: model-based and fuzzy logic rule. The goal of AAN is to provide the required extra torque because of the robot’s dynamics and external load compared to the human limb free movement. The human–robot adaptation is simulated using a nonlinear model predictive controller (NMPC) as the human central nervous system (CNS) for three conditions of initial (the initial session of wearing the robot, without any previous experience), short-term (the entire first session, e.g., 45 min), and long-term experiences. The results showed that the two methods (model-based and fuzzy logic) outperform the traditional proportional method in providing AAN by considering distinctive human and robot models. Additionally, the CNS actuator model has difficulty in the initial experience and activates both antagonist and agonist muscles to reduce movement oscillations. In the long-term experience, the simulation shows no oscillation when the CNS NMPC learns the robot model and modifies its weights to simulate realistic human behavior. We found that the desired strength of the robot should be increased gradually to ignore unexpected human–robot interactions (e.g., robot vibration, human spasticity). The proposed mid-level controllers can be used for wearable assistive devices, exoskeletons, and rehabilitation robots.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-Based Mid-Level Regulation for Assist-As-Needed Hierarchical Control of Wearable Robots: A Computational Study of Human-Robot Adaptation

2022

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“…Currently, robots have been widely used in various fields, such as an industrial plant (Björnsson et al, 2018;Lamon et al, 2019;Rodrıguez et al, 2019;Raessa et al, 2020), medical healthcare (Tavakoli et al, 2020;Yang et al, 2020), rehabilitation exoskeleton (Li et al, 2019(Li et al, , 2020, space exploration (Papadopoulos et al, 2021) and it has great advantages on the repetitive accuracy and reducing the cost. Nowadays, robots are expected to perform more challenging tasks, such as medical scanning, and composite layup as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Composite Layup Skill Learning and Generalizing Through Teleoperation

Wang

et al. 2022

Front. Neurorobot.

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In this article, an impedance control-based framework for human-robot composite layup skill transfer was developed, and the human-in-the-loop mechanism was investigated to achieve human-robot skill transfer. Although there are some works on human-robot skill transfer, it is still difficult to transfer the manipulation skill to robots through teleoperation efficiently and intuitively. In this article, we developed an impedance-based control architecture of telemanipulation in task space for the human-robot skill transfer through teleoperation. This framework not only achieves human-robot skill transfer but also provides a solution to human-robot collaboration through teleoperation. The variable impedance control system enables the compliant interaction between the robot and the environment, smooth transition between different stages. Dynamic movement primitives based learning from demonstration (LfD) is employed to model the human manipulation skills, and the learned skill can be generalized to different tasks and environments, such as the different shapes of components and different orientations of components. The performance of the proposed approach is evaluated on a 7 DoF Franka Panda through the robot-assisted composite layup on different shapes and orientations of the components.

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“…However, this control method requires accurate modeling of the controlled object, and it is also very challenging to control the surgical robot. Therefore, in laparoscopic applications, there are only very few and old studies at present [34][35][36][37]. Nevertheless, in the case that the motion equation of the robot is clear, MPC has the following significant advantages.…”

Section: Introductionmentioning

confidence: 99%

Incorporating model predictive control with fuzzy approximation for robot manipulation under remote center of motion constraint

Zhang

She

et al. 2021

Complex Intell. Syst.

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Remote center of motion (RCM) constraint has attracted many research interests as one of the key challenges for robot-assisted minimally invasive surgery (RAMIS). Although it has been addressed by many studies, few of them treated the motion constraint with an independent workspace solution, which means they rely on the kinematics of the robot manipulator. This makes it difficult to replicate the solutions on other manipulators, which limits their population. In this paper, we propose a novel control framework by incorporating model predictive control (MPC) with the fuzzy approximation to improve the accuracy under the motion constraint. The fuzzy approximation is introduced to manage the kinematic uncertainties existing in the MPC control. Finally, simulations were performed and analyzed to validate the proposed algorithm. By comparison, the results prove that the proposed algorithm achieved success and satisfying performance in the presence of external disturbances.

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Human-in-the-Loop Control of a Wearable Lower Limb Exoskeleton for Stable Dynamic Walking

Cited by 75 publications

References 31 publications

Model-Based Mid-Level Regulation for Assist-As-Needed Hierarchical Control of Wearable Robots: A Computational Study of Human-Robot Adaptation

Model-Based Mid-Level Regulation for Assist-As-Needed Hierarchical Control of Wearable Robots: A Computational Study of Human-Robot Adaptation

A Framework for Composite Layup Skill Learning and Generalizing Through Teleoperation

Incorporating model predictive control with fuzzy approximation for robot manipulation under remote center of motion constraint

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