Fuzzy variable impedance-based adaptive neural network control in physical human–robot interaction

Liu, Andong; Chen, Tao; Zhu, Huiling; Fu, Minglei; Xu, Jianming

doi:10.1177/09596518221128088

Cited by 8 publications

(8 citation statements)

References 34 publications

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“…Recent studies have made significant strides in this domain, addressing cognitive ergonomics in collaborative robotics [ 1 ], the role of robots in service industries [ 2 ], and skill-learning frameworks for manipulation tasks [ 3 ]. Advances in technology, such as AR-assisted deep reinforcement learning [ 4 ], proactive collaboration models [ 5 ], and adaptive neural network control in physical HRI [ 6 ], are enhancing mutual-cognitive capabilities and safety. Moreover, the intersection of cognitive neuroscience and robotics [ 7 ] is paving the way for future research directions.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Engagement Detection for Human–Robot Interaction in Complex Environments

Lu,

Lo,

Hong

et al. 2024

Sensors

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This study develops a comprehensive robotic system, termed the robot cognitive system, for complex environments, integrating three models: the engagement model, the intention model, and the human–robot interaction (HRI) model. The system aims to enhance the naturalness and comfort of HRI by enabling robots to detect human behaviors, intentions, and emotions accurately. A novel dual-arm-hand mobile robot, Mobi, was designed to demonstrate the system’s efficacy. The engagement model utilizes eye gaze, head pose, and action recognition to determine the suitable moment for interaction initiation, addressing potential eye contact anxiety. The intention model employs sentiment analysis and emotion classification to infer the interactor’s intentions. The HRI model, integrated with Google Dialogflow, facilitates appropriate robot responses based on user feedback. The system’s performance was validated in a retail environment scenario, demonstrating its potential to improve the user experience in HRIs.

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Section: Introductionmentioning

confidence: 99%

Implementation of Engagement Detection for Human–Robot Interaction in Complex Environments

Lu,

Lo,

Hong

et al. 2024

Sensors

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“…Xie et al (2019) used particle swarm optimization algorithm to optimize impedance parameters. Liu et al (2022) used a fuzzy variable impedance control strategy to optimize impedance parameters in different motion states. Shen et al (2022) adjust the impedance parameters, such as damping and stiffness, by fuzzy controller.…”

Section: Introductionmentioning

confidence: 99%

Position‐based force tracking adaptive impedance control strategy for robot grinding complex surfaces system

Wang

Zhu

et al. 2023

Journal of Field Robotics

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As a key technology of robot grinding, force control has great influence on grinding effects. Based on the traditional impedance control, a position‐based force tracking adaptive impedance control strategy is proposed to improve the grinding quality of aeroengine complex curved parts, which considers the stiffness damping environmental interaction model, modifies the reference trajectory by a Lyapunov‐based approach to realize the adaptive grinding process. In addition, forgotten Kalman filter based on six‐dimensional force sensor is used to denoise the force information and a three‐step gravity compensation process including static base value calculation, dynamic zero update and contact force real‐time calculation is proposed to obtain the accurate contact force between tool and workpiece in this method. Then, to verify the effectiveness of the proposed method, a simulation experiment which including five different working conditions is conducted in MATLAB, and the experiment studying the deviation between the reference trajectory and the actual position is carried out on the robot grinding system. The results indicate that the position‐based force tracking adaptive impedance control strategy can quickly respond to the changes of environmental position, reduce the fluctuation range of contact force in time by modifying the reference trajectory, compensate for the defect of the steady‐state error of the traditional impedance control strategy and improve the surface consistency of machined parts.

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“…However, the level of patient disability is different from each other and also for the same patient during the treatment period. 16,17 Various methods used for interaction adjustment including the adaptive fuzzy 18 have been proposed to control the robots and provide the required assistance according to the level of disability and the patient cooperation. 19 In this regard, terms such as assist as needed (AAN) 20 or patient cooperation are used.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode controller for a hybrid lower limb rehabilitation robot with fuzzy adjustment of impedance interaction: A patient cooperation approach

Imani

Najafi

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this study, a sliding mode controller with an adaptive fuzzy impedance model is proposed to improve the human–robot cooperation and promote lower limb rehabilitation performance using a hybrid robot including an orthosis and a programmable plate, designed and built at the University of Guilan. Orthosis controls the patient in the path of the joints, and the plate is considered instead of a treadmill to prevent impact on the soles of the feet. Since the patient’s cooperation to regain motor skills is of particular importance in the training process, the proposed controller tracks the path of the joints while considering the patient will. The controller consists of an adaptive impedance control whose coefficients are adjusted using a fuzzy logic. In fact, the impedance model defines the relationship between the robot and subject. Using the model, the subject force is converted to a virtual displacement and, then, added to the tracking error, then a sliding mode controller tracks the modified path. Moreover, the Lyapunov function is used to guaranty the stability and the sign function which depends on the patient cooperation. To evaluate the performance of the proposed method, the control model is implemented on a healthy person and, then, is compared with the sliding mode controller and constant impedance sliding mode controller. The results show that this method while following the path of joints is able to consider the subject cooperation and overcome nonlinear terms while chattering is low.

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Fuzzy variable impedance-based adaptive neural network control in physical human–robot interaction

Cited by 8 publications

References 34 publications

Implementation of Engagement Detection for Human–Robot Interaction in Complex Environments

Implementation of Engagement Detection for Human–Robot Interaction in Complex Environments

Position‐based force tracking adaptive impedance control strategy for robot grinding complex surfaces system

Sliding mode controller for a hybrid lower limb rehabilitation robot with fuzzy adjustment of impedance interaction: A patient cooperation approach

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