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Passive Model-Predictive Impedance Control for Safe Physical Human–Robot Interaction

文献类型：期刊论文

作者Cao, Ran1,2; Cheng, Long1,2; Li, Houcheng2
刊名IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
出版日期2024-04-01
卷号16期号:2页码:426-435
关键词Impedance control model-predictive control (MPC) passivity physical human-robot interaction
ISSN号2379-8920
DOI10.1109/TCDS.2023.3275217
通讯作者Cheng, Long(long.cheng@ia.ac.cn)
英文摘要Various cognitive systems have been designed to model the position and stiffness profiles of human behavior and then to drive robots by mimicking the human's behavior to accomplish physical human-robot interaction tasks through a properly designed impedance controller. However, some studies have shown that variable stiffness parameters of the impedance controller can cause the violation of the passivity constraint of the robot states, and make the robot's stored energy exceed the external energy injected from the human user, thus leading to the unsafe human-robot interaction. To solve this problem, this article proposes a novel passive model-predictive impedance control method including two control loops. In the bottom-loop of the proposed controller, the robot is driven by a variable impedance controller to achieve the desired compliant interaction behavior. In the top-loop of the proposed controller, the model-predictive control (MPC) is used to ensure that the robot states satisfy the passivity constraint by calculating a complementary torque to limit the stored energy of the robot. The passivity of the closed-loop robot system and the feasibility of MPC are guaranteed by theoretical analysis, ensuring the safety of the robotic movement in the human-robot interaction. The effectiveness of the proposed method is demonstrated by the simulation and experiment on the Franka Emika Panda robot.
WOS关键词ROBOT INTERACTION ; SYSTEMS
资助项目National Key Research and Development Program
WOS研究方向Computer Science ; Robotics ; Neurosciences & Neurology
语种英语
WOS记录号WOS:001197861000007
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program
源URL[http://ir.ia.ac.cn/handle/173211/58271]  
专题多模态人工智能系统全国重点实验室_医疗机器人
通讯作者Cheng, Long
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Cao, Ran,Cheng, Long,Li, Houcheng. Passive Model-Predictive Impedance Control for Safe Physical Human–Robot Interaction[J]. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,2024,16(2):426-435.
APA Cao, Ran,Cheng, Long,&Li, Houcheng.(2024).Passive Model-Predictive Impedance Control for Safe Physical Human–Robot Interaction.IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,16(2),426-435.
MLA Cao, Ran,et al."Passive Model-Predictive Impedance Control for Safe Physical Human–Robot Interaction".IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 16.2(2024):426-435.

入库方式： OAI收割

来源：自动化研究所

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