Adaptive neural network control of robot with passive last joint

Authors:
Chenguang Yang;Zhijun Li;Jing Li;Alex Smith
Affiliations:
School of Computing and Mathematics, Plymouth University, UK;Department of Automation, Shanghai Jiao Tong University, Shanghai, China, College of Automation Science and Engineering, South China University of Technology, China;School of Computing and Mathematics, Plymouth University, UK, Department of Mathematics, Xidian University, China;School of Computing and Mathematics, Plymouth University, UK
Venue:
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part III
Year:
2012

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Abstract

Adaptive control of a robot manipulator with a passive joint (which has neither an actuator nor a holding brake) is investigated. With the aim to shape the controlled manipulator dynamics to be of minimized motion tracking errors and joint accelerations, we employ a linear quadratic regulator (LQR) optimization technique to obtain an optimal reference model. Adaptive neural network (NN) control has been developed to ensure the reference model can be matched in finite time, in the presence of various uncertainties.