Radial Basis Function Network Compensators for Uncertainties of Robotic Manipulators
Ziauddin, S.M. and Zalzala, A.M.S.
(1994)
Radial Basis Function Network Compensators for Uncertainties of Robotic Manipulators.
Research Report.
ACSE Research Report 514
.
Department of Automatic Control and Systems Engineering
Abstract
This report proposes a decentralised compensation scheme for uncertainties and modelling errors of robotic manipulators. The scheme employs a central decoupler and independent joint neural network controllers. Recursive Newton Euler formulas are used to decouple robot dynamics to obtain a set of equations in terms of each joint's input and output. To identify and suppress the effects of uncertainties associated with the model, each joint is controlled separately by Gaussian radial basis function network controllers using direct adaptive techniques. The effectiveness of the proposed adaptive control scheme is demonstrated by controlling the three primary joints of PUMA 560. Simulation results show that this control scheme can achieve fast and precise robot motion control under substantial payload variations.
Metadata
Item Type:
Monograph
Authors/Creators:
Ziauddin, S.M.
Zalzala, A.M.S.
Copyright, Publisher and Additional Information:
The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
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