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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Fast and accurate positioning and swing minimization of the containers and other loads in crane manipulation are demanding and in the same time conflicting tasks. For accurate positioning, the main problem is nonlinear friction compensation, especially in the low speed region. In this paper authors propose position controller realized as hybrid controller. It consists of the conventional linear state feedback controller with additional friction selflearning neural compensator in the feedforwad loop. Selflearning compensator is based on the B-spline artificial neural network which consists of the one hidden layer of the B-spline second order functions. The experimental results show that friction compensator is able to remove position error in steady state.