DocumentCode :
2440624
Title :
Impedance Control of Exoskeleton Suit Based on Adaptive RBF Neural Network
Author :
Yang, Zhiyong ; Zhu, Yuguang ; Yang, Xiuxia ; Zhang, Yuanshan
Author_Institution :
Dept. of Strategy Missle Eng., Naval Aeronaut. & Astronaut. Univ., Yantai, China
Volume :
1
fYear :
2009
fDate :
26-27 Aug. 2009
Firstpage :
182
Lastpage :
187
Abstract :
Exoskeleton suit is a typical human-machine system. Control the exoskeleton suit to track the pilot´s moving trajectory as well as to minimize the human-machine interaction force. The suit will help decrease the pilot´s power consumption and assist the pilot to carry heavy load. Impedance control was introduced to the control of exoskeleton suit. As the control laws that based on the dynamic model without model uncertainty compensation will increase the human-machine force, a RBF neural network with adaptive learning algorithm was used to compensate the model uncertainty. The stability analysis of the control law was given and the simulation results show the feasibility and validity of the proposed control law.
Keywords :
human-robot interaction; neurocontrollers; robot dynamics; stability; adaptive RBF neural network; adaptive learning algorithm; exoskeleton robot; exoskeleton suit; human-machine interaction force; human-machine system; impedance control; stability analysis; Adaptive control; Adaptive systems; Exoskeletons; Force control; Impedance; Man machine systems; Neural networks; Programmable control; Trajectory; Uncertainty; RBF neural network; exoskeleton suit; human-machine system; impedance control; uncertainties;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics, 2009. IHMSC '09. International Conference on
Conference_Location :
Hangzhou, Zhejiang
Print_ISBN :
978-0-7695-3752-8
Type :
conf
DOI :
10.1109/IHMSC.2009.54
Filename :
5336076
Link To Document :
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