TY - JOUR
T1 - A Bilateral Teleoperation System With Learning-Based Cognitive Guiding Force
AU - Ma, Zhiqiang
AU - Shi, Dailiang
AU - Liu, Zhengxiong
AU - Yu, Jianhui
AU - Huang, Panfeng
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - This article investigates the problem involving a fuzzy logic system (FLM)-based bilateral teleoperation system with a unified impedance/admittance structure and learning-based assisted cognitive guiding force, enhancing a smooth transition between augmentation and autonomous motion on the master side. The impedance structure regulates the impedance and admittance via the parameters determined by the FLM and, besides the variable impedance during interaction with the environment, the fuzzy Q -learning algorithm generates an assisted cognitive guiding force to guide the trainee operator in free space for reaching the interaction surface smoothly and steadily. The degeneration of tracking performance in the bilateral teleoperation system caused by time delay is analyzed and reduced via a mixed-type error. Experimental results verify the stability analyses, and under the proposed control scheme, the human-robot-environment interaction is smooth and stable.
AB - This article investigates the problem involving a fuzzy logic system (FLM)-based bilateral teleoperation system with a unified impedance/admittance structure and learning-based assisted cognitive guiding force, enhancing a smooth transition between augmentation and autonomous motion on the master side. The impedance structure regulates the impedance and admittance via the parameters determined by the FLM and, besides the variable impedance during interaction with the environment, the fuzzy Q -learning algorithm generates an assisted cognitive guiding force to guide the trainee operator in free space for reaching the interaction surface smoothly and steadily. The degeneration of tracking performance in the bilateral teleoperation system caused by time delay is analyzed and reduced via a mixed-type error. Experimental results verify the stability analyses, and under the proposed control scheme, the human-robot-environment interaction is smooth and stable.
KW - Bilateral teleoperation system
KW - cognitive guiding force
KW - fuzzy Q-learning algorithm
KW - physical human-robot interaction
KW - physical human-robot-environment interaction
KW - sliding mode control
UR - http://www.scopus.com/inward/record.url?scp=85149399574&partnerID=8YFLogxK
U2 - 10.1109/TCDS.2023.3245216
DO - 10.1109/TCDS.2023.3245216
M3 - 文章
AN - SCOPUS:85149399574
SN - 2379-8920
VL - 15
SP - 2214
EP - 2227
JO - IEEE Transactions on Cognitive and Developmental Systems
JF - IEEE Transactions on Cognitive and Developmental Systems
IS - 4
ER -