NDLI: Stable neural PD controller for redundantly actuated parallel manipulators with uncertain kinematics

Content Provider	IEEE Xplore Digital Library
Author	Loreto, G. Garrido, R.
Copyright Year	2005
Description	Author affiliation: Departamento de Control Automático, CINVESTAV-IPN, Av.IPN 2508 México D.F., 07360, México, fax: (52) 55 57 47 70 89, gloreto@ctrl.cinvestav.mx (Loreto, G.)
Abstract	This paper proposes a stable Proportional Derivative Controller applied to redundantly actuated parallel robots with uncertainty in the kinematic parameters. It is shown that all the closed loop signals are uniformly ultimately bounded. Gravitational terms are approximated using a Radial Basis Function Neural Network with joint information feeding their activation functions and with on-line real-time learning. A depart from current approaches is the fact that damping is added at the joint level using the robot active joints and the fact that it does not require the exact knowledge of the kinematic parameters. The learning rule for the neural network weights is obtained from a Lyapunov stability analysis. Simulation results are reported and demonstrate the effectiveness of the proposed controller.
Starting Page	2035
Ending Page	2040
File Size	3133962
Page Count	6
File Format	PDF
ISBN	0780395670
DOI	10.1109/CDC.2005.1582460
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-12-15
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	IEEE/EUCA
Subject Keyword	PD control Kinematics Manipulators Proportional control Parallel robots Uncertainty Radial basis function networks Damping Neural networks Lyapunov method actuator redundancy Radial basis function regulation parallel robots
Content Type	Text
Resource Type	Article

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