engleski

Control of 3D Tower Crane based on Tensor Product Transformation with Neural Friction Compensation

Fast and accurate positioning and swing minimization of heavy loads in crane manipulation are demanding and, in the same time, conflicting tasks. For accurate positioning, the main problem is nonlinear friction effect, especially in the low speed region. In this paper authors propose a control scheme for 3D tower crane, that consists of the tensor product transformation based nonlinear feedback controller, with 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 3D laboratory crane model are given.

3D tower crane; neural network; non-PDC control law; friction compensation; RBF network; on-line network learning

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