Identification and control of crane swing reduction using artificial neural network
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namdvph@hcmute.edu.vnKeywords:
Identification, control, crane swing reduction, artificial neural networkAbstract
This paper presents crane swing reduction control method by identifying the capacity of an artificial neural network. The mathematical model of crane is built from physical laws using Lagrange method. This model is used to find responses of the system, and to simulate identification and control process. A recurrent artificial neural network is used to identify and control the crane to reduce its load swing by supervised learning. Identification and control are done in simulation and implemented in a real system. Results of simulation and application in the real show that an artificial neural network can very well identify and control the crane swing reduction
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References
Solihin M.I., Wahyudi, Sensorless Anti- swing Control for Automatic Gantry Crane System: Model-based Approach, International Journal of Applied Engineering Research, 2007.
Solihin M.I., Wahyudi, Sensorless anti- swing control of automatic gantry crane using Dynamic Recurrent Neural Network- based soft sensor, Int. J. Intelligent Systems Technologies and Applications, 2009, pp. 112–127.
King Shyang Sien, Command shaping control for a crane system, University technology Malaysia, 2006.
Tim Callinan, Artificial neural network identification and control of the inverted pendulum, August 2003.
Oludele Awodele, Olawale Jegede, Neural Networks and Its Application in Engineering, Proceedings of Informing Science & IT Education Conference, 2009.
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