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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks Volume 18
Editor
Cihan H. Dagli
Cihan H. Dagli
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ISBN-10:
0791802823
ISBN:
9780791802823
No. of Pages:
700
Publisher:
ASME Press
Publication date:
2008

An intelligent tracking control system is designed for nonlinear robot manipulator. The controller which is implemented into the trajectory planner utilizes a recurrent self constructing RBF network in order to capture the system dynamics. The structure learning algorithm creates online new hidden neurons to increase the learning ability of the controller and removes insignificant neurons to reduce the computation load. The adaptive laws are derived in the sense of Lyapunov so that the whole closed loop is stable with no restrictive conditions on the design constants for the stability. A comparative analysis is performed between this controller and a feed-forward self constructing RBF controller in case of Linear Segments with Parabolic Blends trajectories tracking. The proposed controller presents higher performance for different cases of uncertainties in manipulator parameters.

Abstract
Introduction
Controller Tracking Design for Manipulator
Structure Learning Phase
Parameters Learning Algorithm
Simulations
Conclusion
References
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