DocumentCode :
1663654
Title :
RLS-ESN based PID control for rehabilitation robotic arms driven by PM-TS actuators
Author :
Wu, Jun ; Huang, Jian ; Wang, Yongji ; Xing, Kexin
Author_Institution :
Dept. of Control Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China
fYear :
2010
Firstpage :
511
Lastpage :
516
Abstract :
To drive a single joint of rehabilitation robotic arm, we propose a new PM-TS actuator comprising a Pneumatic Muscle (PM) and a Torsion Spring (TS). Unlike the traditional agonist/antagonist PM actuator, the PM is arranged in appropriate place as agonist and the torsion spring provides opposing torque as antagonist in the proposed actuator. The 1-DOF and 2-DOF rehabilitation robotic arm models are derived considering the PM-TS dynamic model. To realize a high-accurate trajectory tracking control of the robotic arms, an intelligent PID controller based on an Echo State Neural Network (ESN) is proposed, where the ESN state is updated by the online Recursive Least Square (RLS) algorithm. Simulation results demonstrate the validity of PM-TS actuators. The performance of RLS-ESN based PID controller is found more satisfactory than conventional PID controller in our study.
Keywords :
least squares approximations; manipulators; medical robotics; neurocontrollers; patient rehabilitation; pneumatic actuators; position control; recursive estimation; springs (mechanical); three-term control; torque control; torsion; PM-TS actuators; RLS algorithm; RLS-ESN; antagonist PM actuator; echo state neural network; intelligent PID controller; online recursive least square algorithm; opposing torque; pneumatic muscle; rehabilitation robotic arms; torsion spring; trajectory tracking control; Recurrent neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Modelling, Identification and Control (ICMIC), The 2010 International Conference on
Conference_Location :
Okayama
Print_ISBN :
978-1-4244-8381-5
Electronic_ISBN :
978-0-9555293-3-7
Type :
conf
Filename :
5553510
Link To Document :
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