Toward Safe Human-Robot Interaction: A Fast-Response Admittance Control Method for Series Elastic Actuator

Author(s):  
Haoran Zhong ◽  
Xinyu Li ◽  
Liang Gao ◽  
Congbo Li
2017 ◽  
Vol 37 (3) ◽  
pp. 296-303 ◽  
Author(s):  
Ningbo Yu ◽  
Wulin Zou

Purpose This paper aims to present an impedance control method with mixed H2/H∞ synthesis and relaxed passivity for a cable-driven series elastic actuator to be applied for physical human–robot interaction. Design/methodology/approach To shape the system’s impedance to match a desired dynamic model, the impedance control problem was reformulated into an impedance matching structure. The desired competing performance requirements as well as constraints from the physical system can be characterized with weighting functions for respective signals. Considering the frequency properties of human movements, the passivity constraint for stable human–robot interaction, which is required on the entire frequency spectrum and may bring conservative solutions, has been relaxed in such a way that it only restrains the low frequency band. Thus, impedance control became a mixed H2/H∞ synthesis problem, and a dynamic output feedback controller can be obtained. Findings The proposed impedance control strategy has been tested for various desired impedance with both simulation and experiments on the cable-driven series elastic actuator platform. The actual interaction torque tracked well the desired torque within the desired norm bounds, and the control input was regulated below the motor velocity limit. The closed loop system can guarantee relaxed passivity at low frequency. Both simulation and experimental results have validated the feasibility and efficacy of the proposed method. Originality/value This impedance control strategy with mixed H2/H∞ synthesis and relaxed passivity provides a novel, effective and less conservative method for physical human–robot interaction control.


2021 ◽  
Vol 11 (12) ◽  
pp. 5651
Author(s):  
Yu Wang ◽  
Yuanyuan Yang ◽  
Baoliang Zhao ◽  
Xiaozhi Qi ◽  
Ying Hu ◽  
...  

In order to achieve effective physical human–robot interaction, human dynamic characteristics needs to be considered in admittance control. This paper proposes a variable admittance control method for physical human–robot interaction based on trajectory prediction of human hand motion. By predicting the moving direction of the robot end tool under human guidance, the admittance control parameters are adjusted to reduce the interaction force. The end tool trajectory of the robot under human guidance is used for offline training of long and short-term memory neural network to generate trajectory predictors. Then the trajectory predictors are used in variable admittance control to predict the trajectory and movement direction of the robot end tool in real time. The variable admittance controller adjusts the damping matrix to reduce the damping value in the moving direction. Experiment results show that, using the constant admittance method as a benchmark, the interaction force of the proposed method is reduced by 23%, the trajectory error is reduced by 51%, and the operating jerk is reduced by at least 21%, which proves that the proposed method improves the accuracy and compliance of the operation.


2019 ◽  
Vol 38 (6) ◽  
pp. 747-765 ◽  
Author(s):  
Federica Ferraguti ◽  
Chiara Talignani Landi ◽  
Lorenzo Sabattini ◽  
Marcello Bonfè ◽  
Cesare Fantuzzi ◽  
...  

Admittance control allows a desired dynamic behavior to be reproduced on a non-backdrivable manipulator and it has been widely used for interaction control and, in particular, for human–robot collaboration. Nevertheless, stability problems arise when the environment (e.g. the human) the robot is interacting with becomes too stiff. In this paper, we investigate the stability issues related to a change of stiffness of the human arm during the interaction with an admittance-controlled robot. We propose a novel method for detecting the rise of instability and a passivity-preserving strategy for restoring a stable behavior. The results of the paper are validated on two robotic setups and with 50 users performing two tasks that emulate industrial operations.


2015 ◽  
Vol 31 (5) ◽  
pp. 1089-1100 ◽  
Author(s):  
Haoyong Yu ◽  
Sunan Huang ◽  
Gong Chen ◽  
Yongping Pan ◽  
Zhao Guo

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