Flatness Control of A Crane
H. Souilem,
H. Mekki,
N. Derbel
Issue:
Volume 1, Issue 1, February 2013
Pages:
1-6
Published:
20 February 2013
Abstract: The aim of this work is to propose a flatness control of a crane detailing adopted mechanisms and approaches in order to be able to control this system and to solve problems encountered during its functioning. The control objective is the sway-free transportation of the crane’s load taking the commands of the crane operator into account. Based on the mathematical model linearizing and stabilizing control laws for the slewing and luffing motion are derived using the input/output linearization approach. The method allows for transportation of the payload to a selected point and ensures minimisation of its swings when the motion is finished. To achieve this goal a mathematical model of the control system of the displacement of the payload has been constructed. A theory of control which ensures swing-free stop of the payload is presented. Selected results of numerical simulations are shown. At the end of this work, a comparative study between the real moving and the desired one has been presented.
Abstract: The aim of this work is to propose a flatness control of a crane detailing adopted mechanisms and approaches in order to be able to control this system and to solve problems encountered during its functioning. The control objective is the sway-free transportation of the crane’s load taking the commands of the crane operator into account. Based on t...
Show More
Robust Generalized Minimum Variance Controller Using Neural Network for Civil Engineering Problems
L. Guenfaf,
M. Djebiri,
M. S. Boucherit,
F. Boudjema
Issue:
Volume 1, Issue 1, February 2013
Pages:
7-15
Published:
20 February 2013
Abstract: This paper presents a robustness of the proposed generalized minimum variance algorithm. The main idea is to use artificial neural network for generalization of the GMV. This will give a neural network-based control method wich can be applied to civil engineering structures. The neural network learns the control task from an already existing controller, which is the generalized minimum variance (GMV) controller. The objective is to take advantage of the generalization capabilities and the nonlinear behavior of neural networks in order to overcome the limitations of the existing controller and even to improve its performances. Simulation results demonstrate the robustness of this algorithm and its capability to compensate the structural parameter variations and seismic ground motion.
Abstract: This paper presents a robustness of the proposed generalized minimum variance algorithm. The main idea is to use artificial neural network for generalization of the GMV. This will give a neural network-based control method wich can be applied to civil engineering structures. The neural network learns the control task from an already existing contro...
Show More
