Compliant Control of Lower Limb Rehabilitation Exoskeleton Robot Based on Flexible Transmission

Liu, Keping; Li, Li; Li, Wanting; Gu, Jian; Sun, Zhongbo

doi:10.1007/s42235-022-00302-0

Cited by 12 publications

(4 citation statements)

References 34 publications

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“…Similarly, the MATLAB function dare() can be used to calculate the weights. Thus, the mathematical model of the follower's DMPC (dynamic movement profile criterion) controller is obtained as shown in Equation (22).…”

Section: Follower Dmpc Controller Designmentioning

confidence: 99%

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Coordinated Transport by Dual Humanoid Robots Using Distributed Model Predictive Control

Wen,

Shi,

2024

Biomimetics

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Dual humanoid robot collaborative control systems possess better flexibility and adaptability in complex environments due to their similar structures to humans. This paper adopts a distributed model predictive controller based on the leader–follower approach to address the collaborative transportation control issue of dual humanoid robots. In the dual-robot collaborative control system, network latency issues may arise due to unstable network conditions, affecting the consistency of dual-robot collaboration. To solve this issue, a communication protocol was constructed through socket communication for dual-robot collaborative consistency, thereby resolving the problem of consistency in dual humanoid robot collaboration. Additionally, due to the complex structure of humanoid robots, there are deficiencies in position tracking accuracy during movement. To address the poor accuracy in position tracking, this paper proposes a distributed model predictive control that considers historical cumulative error, thus enhancing the position tracking accuracy of dual-robot collaborative control.

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Section: Follower Dmpc Controller Designmentioning

confidence: 99%

“…The right arm of the NAO robot, for instance, possesses five degrees of freedom and is a type of serial kinematic structure. According to the Denavit–Hartenberg (D-H) convention [ 22 ], the data presented in Table 2 can be derived. On this basis, a linkage coordinate system for the NAO robot’s right arm is established.…”

Section: Robot Motion Modelmentioning

confidence: 99%

Coordinated Transport by Dual Humanoid Robots Using Distributed Model Predictive Control

Wen,

Shi,

2024

Biomimetics

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“…3) Adaptation to obstacle environments: Thanks to its randomness, the RRT algorithm can automatically circumvent obstacles and find safe paths, adapting to complex environments [14].…”

Section: Principles Of the Rrt Algorithmmentioning

confidence: 99%

Path planning for storage robots using the RRT algorithm

Zhang

2023

TNS

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This paper delves into the issue of storage robot path planning, a critical challenge in the field of warehouse automation. To address this challenge, this paper introduces the Rapidly-exploring Random Tree (RRT) algorithm and presents an innovative path planning approach aimed at efficiently handling dynamic environments and multiple constraints.Firstly, this paper extensively explores the complexity of the storage robot path planning problem, emphasizing its crucial nature in practical applications. Next, it provides an in-depth explanation of the principles of the RRT algorithm, particularly highlighting its unique advantages in path planning, especially in high-dimensional spaces and nonlinear constraints. In the implementation section, a detailed description is provided on how to apply the RRT algorithm to storage robot path planning, including the workflow of the algorithm and its key steps.In the discussion section, this paper thoroughly analyzes the potential application value of the RRT algorithm in storage robot path planning and discusses future improvement directions and research areas. Finally, by summarizing the outcomes of this study, the paper emphasizes the pivotal role of the RRT algorithm in storage robot path planning and the positive impact it brings to solving this significant problem.

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“…The experimental results demonstrate that this method can achieve satisfactory trajectory tracking error and interaction torque in human exoskeleton collaborative motion and has good compliance. Liu et al (2023) designed a knee exoskeleton connected in series with a soft shaft and a steel shaft to achieve flexible force transfer and developed a sliding mode impedance controller to enable the knee exoskeleton to stably track trajectory while achieving compliance control. The experiment has verified that the sliding-mode impedance controller can ensure accurate and compliant control of the knee exoskeleton in actual environments.…”

Section: Introductionmentioning

confidence: 99%

AEKF-based trajectory-error compensation of knee exoskeleton for human–exoskeleton interaction control

Zhang,

Cao,

et al. 2024

RIA

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Purpose The purpose of this paper is to design a new trajectory error compensation method to improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction motion. Design/methodology/approach A trajectory error compensation method based on admittance-extended Kalman filter (AEKF) error fusion for human–exoskeleton interaction control. The admittance controller is used to calculate the trajectory error adjustment through the feedback human–exoskeleton interaction force, and the actual trajectory error is obtained through the encoder feedback of exoskeleton and the designed trajectory. By using the fusion and prediction characteristics of EKF, the calculated trajectory error adjustment and the actual error are fused to obtain a new trajectory error compensation, which is feedback to the knee exoskeleton controller. This method is designed to be capable of improving the trajectory tracking performance of the knee exoskeleton and enhancing the compliance of knee exoskeleton interaction. Findings Six volunteers conducted comparative experiments on four different motion frequencies. The experimental results show that this method can effectively improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction. Originality/value The AEKF method first uses the data fusion idea to fuse the estimated error with measurement errors, obtaining more accurate trajectory error compensation for the knee exoskeleton motion control. This work provides great benefits for the trajectory tracking performance and compliance of lower limb exoskeletons in human–exoskeleton interaction movements.

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Compliant Control of Lower Limb Rehabilitation Exoskeleton Robot Based on Flexible Transmission

Cited by 12 publications

References 34 publications

Coordinated Transport by Dual Humanoid Robots Using Distributed Model Predictive Control

Coordinated Transport by Dual Humanoid Robots Using Distributed Model Predictive Control

Path planning for storage robots using the RRT algorithm

AEKF-based trajectory-error compensation of knee exoskeleton for human–exoskeleton interaction control

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