An adaptive tracking controller for parallel robotic manipulators based on fully tuned radial basic function networks

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• 作者：Tien Dung Le^a ; ^{dung.letien@gmail.com" class="auth_mail}Author Vitae ; Hee-Jun Kang^b ; ^{hjkang@ulsan.ac.kr" class="auth_mail}Author Vitae
• 关键词：Adaptive tracking control ; Radial basis function network ; Fully tuned ; Online tuning ; Parallel manipulator
• 刊名：Neurocomputing
• 出版年：5 August 2014
• 年：2014
• 卷：137
• 期：Complete
• 页码：12-23
• 全文大小：1209 K

文摘

Parallel robotic manipulators have a complicated dynamic model due to the presence of multi-closed-loop chains and singularities. Therefore, the control of them is a challenging and difficult task. In this paper, a novel adaptive tracking controller is proposed for parallel robotic manipulators based on fully tuned radial basis function networks (RBFNs). For developing the controller, a dynamic model of a general parallel manipulator is developed based on D壮Alembert principle and principle of virtual work. RBFNs are utilized to adaptively compensate for the modeling uncertainties, frictional terms and external disturbances of the control system. The adaptation laws for the RBFNs are derived to adjust on-line the output weights and both the centers and variances of Gaussian functions. The stability of the closed-loop system is ensured by using the Lyapunov method. Finally, a simulation example is conducted for a 2 degree of freedom (DOF) parallel manipulator to illustrate the effectiveness of the proposed controller.