Journal
ASIAN JOURNAL OF CONTROL
Volume 17, Issue 2, Pages 443-458Publisher
WILEY
DOI: 10.1002/asjc.986
Keywords
3D tower crane; neural network; non-PDC control law; friction compensation; RBF network; on-line network learning
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Funding
- European Commission [285939]
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Fast and accurate positioning and swing minimization of heavy loads in crane manipulation are demanding and, at the same time, conflicting tasks. Accurate load positioning is primarily limited by the existence of a nonlinear friction effect, especially in the low speed region. In this paper the authors propose a new control scheme for 3D tower crane, that consists of a tensor product model transformation based nonlinear feedback controller, with an additional neural network based friction compensator. Tensor product based controller is designed using linear matrix inequalities utilizing a parameter varying Lyapunov function. Neural network parameters adaptation law is derived using Lyapunov stability analysis. The simulation and experimental results on a 3D laboratory crane model are presented.
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