DETERMINATION OF THE NEXT STOPPING FLOOR IN ELEVATOR TRAFFIC CONTROL BY MEANS OF NEURAL NETWORKS

Year 2006, Volume: 6 Issue: 1, 27 - 33, 02.01.2012

C.erdem İmrak Mustafa Özkırım

Abstract

When a group of lifts serve together it is important coordinate the movements of the individual lifts in such a way that the lift group should operate efficiently. This is dealt with elevator control systems, which have become more and more complicated due to their nature of intelligence. Neural networks, which have been proved to be successful in many fields, can also be applied to the next stopping floor problem in elevator traffic control algorithms. In particular, neural networks can offer better solutions to the next stopping floor problem when compared to the classical traffic control methods. Elevator control algorithms based on neural networks can dynamically learn the behavior of an elevator system and predict the next floors to stop by considering what has been learnt by processing the changes in passenger service demand pattern. Neural networks have been used to build a one step ahead predictor for elevator traffic pattern. In this paper a neural network algorithm is apllied to obtain a better solution to the next stopping floor problem in elevator group control and its learning capability is assessed by means of simulation software developed.

Keywords

backpropagation, elevator control, group control, next stopping floor, neural networks

References

• Barney, G.C., “Elevator Traffic Handbook”, Spon Press, London, 2003.
• Waering, M., “A Network For Lift Status Monitoring”, MSc. Dissertation, UMIST, UK, Alexandris, N., Chrissikopoulos, V. and Vassilacopoulos, G., “Lifts- An Expert System For Lift System Design”, Elevator Technology 2, IAEE Publ., pp. 1-9, 1988.
• Prowse, R.W., Thomson, T. and Howells, D., “Design and Control of Lift Systems Using Expert Systems and Traffic Sensing”, Elevator Technology 4, IAEE Publ., pp.219-226, 1992.
• Al-Sharif, L.R., “Predictive Methods in Lift Traffic Analysis”, PhD thesis, UMIST, UK, Ho, M. and Robertson, B., “Elevator Group Supervisory Control Using Fuzzy Logic”, Canadian Conference on Elevator and Computer Engineering, 2, 11.4.4, 1994.
• Qun, Z., Ding, S., Yu, C. and Xiaofeng, L., “Elevator Group Control System Modeling Based on Object Oriented Petri Net”, Elevator World Magazine, 2001.
• Chan W.L. and So, A.T.P., “Dynamic Zoning for Intelligent Supervisory Control”, Int. to Elevator Engineering, Vol.1, pp. 1-10, 1996.
• Mukherjee, A. and Deshpande, J.M., “Application of Artificial Neural Networks In Structural Design Expert Systems, Computer & Structures, Vol.54, No.3, pp. 367-375, 1995.
• Siikonen, M-L., “On Traffic Planning Methodology”, Elevator Technology 10, IAEE Publ., pp.267-274, 2000.
• Closs, G.D., “The Computer Control of Passenger Traffic in Large Lift Systems”, PhD Thesis, UMIST, UK, 1970.
• Imrak, C.E., “Elevator Control Systems and Traffic Analysis”, Proceedings of 7th International Machine Design and Manufacturing Congress, Ankara, pp. 351-360, 1996.
• Rooney, M.F. and Smith, S.E., “Artificial Intelligence in Engineering Design”, Comput. Struct., Vol.16, pp. 279-288, 1983.
• Miravete, A.,”New Materials and New Technologies Applied to Elevators”, Elevator World Inc., 2002.
• Imrak, C.E., “Traffic Analysis, Design and Simulation of Elevator Systems”, PhD. Thesis, ITU, Istanbul, 1996.
• Imrak, C.E. and Ozkırım, M., “The Modeling And Simulation Of Elevator Group Control Systems For Public Service Buildings, Preprints the 3rd IFAC Workshop DECOM-TT , Istanbul, pp. 159-164, 2003.
• Korn, A.G., “Neural Networks and Fuzzy- Logic Control on Personal Computers and Workstations”, MIT Press, London, 1995.
• Lisboa, R.G., “Neural Network Current Application”, Chapman and Hall Pub., New York, 1992.
• Rumelhart, D.E. and McClelland, J.L., “Parallel Distributed Processing, Vols 1 and 2”, MIT Press, Cambridge, 1986.
• Lippmann, R.P., “An Introduction to Computing with Neural Nets”, IEEE ASSP Magazine, Vol.4, pp. 4-22, 1987.
• Rumelhart,D.E., Hinton, G.E. and Williams, R.J., “Learning Representations by Backpropagation Errors”, Nature, Vol.323, pp. 536, 1986.
• Barney, G.C. and Dos Santos, S.M., “Elevator Traffic Analysis, Design and Control”, Peter Peregrinus Ltd., London, 1985.
• Imrak, C.E. and Barney, G.C., “Application of Neural Networks on Traffic Control”, Elevator Technology 9, IAEE Publ., pp. 140- , 1998.
• C.Erdem İMRAK has been employed as a Lecturer in Istanbul Technical University