Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-26T01:05:41.108Z Has data issue: false hasContentIssue false
  • English
  • Français

Adaptable Fuzzy Expert System for Ship Lock Control Support

Published online by Cambridge University Press:  03 May 2016

Todor Bačkalić Open the ORCID record for Todor Bačkalić [Opens in a new window] ,
Vladimir Bugarski Open the ORCID record for Vladimir Bugarski [Opens in a new window] ,
Filip Kulić  and
Željko Kanović
Todor Bačkalić*
Affiliation:
(University of Novi Sad, Faculty of Technical Sciences)
Vladimir Bugarski
Affiliation:
(University of Novi Sad, Faculty of Technical Sciences)
Filip Kulić
Affiliation:
(University of Novi Sad, Faculty of Technical Sciences)
Željko Kanović
Affiliation:
(University of Novi Sad, Faculty of Technical Sciences)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

A ship lock zone represents a specific area on waterway, and control of the ship lockage process requires a comprehensive approach. This research is a practical application of a Mamdani-type fuzzy inference system and particle swarm optimisation to control this process. It presents an optimisation process that adapts control logic to the desired criteria. The initially proposed Fuzzy Expert System (FES) was developed using suggestions from lockmasters (ship lock operators) with extensive experience. Further optimisation of the membership function parameters of the input variables was performed to achieve better results in the local distribution of ship arrivals. The presented fuzzy logic-based expert system was designed as part of a Programmable Logic Controller (PLC) and Supervisory Control And Data Acquisition (SCADA) system to support decision making and control. The developed fuzzy algorithm is a rare application of artificial intelligence in navigable canals and significantly improves performance of the ship lockage process. This adaptable FES is designed to be used as a support in decision-making processes or for the direct control of ship lock operations.

Keywords

Ship lockVessel trafficFuzzy expert systemOptimisation
Type
Research Article
Information
The Journal of Navigation , Volume 69 , Issue 6 , November 2016 , pp. 1341 - 1356
Copyright
Copyright © The Royal Institute of Navigation 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Adeli, H. (1988). Expert Systems in Construction and Structural Engineering, Chapman and Hall, New York.Google Scholar
Adeli, H. and Balasubramanyam, K.V. (1988). A Novel Approach to Expert Systems for Design of Large Structures, AI Magazine, 9(4), 54–63.Google Scholar
Ankur, M., Debanjan, D., Mehta, S.P., Shalivahan, S. and Bhattacharya, B.B. (2011). PSO vs. GA vs. VFSA: A Comparison of Performance, Accuracy and Resolution with Respect to Inversion of SP Data, Proceedings of Japan Geoscience Union Meeting 2011, Makuhari, Chiba Japan.Google Scholar
Aytug, H. and Koehler, G.J. (2007). The Effect of Multiple Optima on the Simple GA Run-time Complexity, European Journal of Operational Research, 178(1), 27–45.CrossRefGoogle Scholar
Bačkalić, T. (2001). Traffic Control on Artificial Waterways of Limited Dimensions in Function of its Throughput Capacity (Upravljanje saobraćajem na veštačlkim plovnim putevima ograničenih dimenzija u funkciji njihove propusne sposobnosti - in original), PhD thesis, University of Novi Sad, Serbia.Google Scholar
Bergh, V.D. (2001). An Analysis of Particle Swarm Optimizers, PhD thesis, University of Pretoria, Faculty of Natural and Agricultural Science.Google Scholar
Bouallegue, S., Haggege, J., Ayadi, M. and Benrejeb, M. (2012). PID-type Fuzzy Logic Controller Tuning based on Particle Swarm Optimisation, Engineering Applications of Artificial Intelligence, 25(3), 484–493.CrossRefGoogle Scholar
Bugarski, V., Bačkalić, T. and Kuzmanov, U. (2013). Fuzzy Decision Support System for Ship Lock Control, Expert Systems with Applications, 40(10), 3953–3960.CrossRefGoogle Scholar
Campbell, J.F., Smith, L.D., Sweeney, I.I., Mundy, R., & Nauss, R.M. (2007). Decision tools for reducing congestion at locks on the upper Mississippi river. 40th Annual Hawaii International Conference on System Sciences, Hawaii, USA.Google Scholar
Chen, C.C. (2006). Design of PSO-based Fuzzy Classification Systems, Tamkang Journal of Science and Engineering, 9(1), 63–70.Google Scholar
Figueiredo, J., Botto, M.A. and Rijo, M. (2013). SCADA System with Predictive Controller Applied to Irrigation Canals, Control Engineering Practice, 21(6), 870–886.Google Scholar
Garcia-Nieto, J., Alba, E. and Olivera, A.C. (2012). Swarm Intelligence for Traffic Light Scheduling: Application to Real Urban Areas, Engineering Applications of Artificial Intelligence, 25(2), 274–283.Google Scholar
Kanović, Ž., Rapaić, M. and Jeličić, Z. (2011). Generalized Particle Swarm Optimisation Algorithm – Theoretical and Empirical Analysis with Application in Fault Detection, Applied Mathematics and Computation, 217(24), 10175–10186.Google Scholar
Kanović, Ž., Rapaić, M. and Jeličić, Z. (2013). The Generalized Particle Swarm Optimization Algorithm: Idea, Analysis, and Engineering Applications, Swarm Intelligence for Electric and Electronic Engineering, Portland, OR, Book News Inc., 237–258.Google Scholar
Kanović, Ž., Bugarski, V. and Bačkalić, T. (2014). Ship Lock Control System Optimization using GA, PSO and ABC: A Comparative Review, PROMET - Traffic&Transportation, 26(1), 23–31.Google Scholar
Kecman, V. (2001). Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models, Massachusetts Institute of Technology.Google Scholar
Kennedy, J. and Eberhart, R.C. (2001). Swarm Intelligence, Morgan Kaufmann Publishers, San Francisco.Google Scholar
Kiencke, U., Nielsen, L., Sutton, R., Schilling, K., Papageorgiou, M. and Asama, H. (2006). The Impact of Automatic Control on Recent Developments in Transportation and Vehicle Systems, Annual Reviews in Control, 30(1), 81–89.Google Scholar
Khisty, C.J. (1996). Waterway traffic analysis of the Chicago River and lock. Maritime Policy and Management, 23(3), 261–270.Google Scholar
Kordon, A.K. (2010). Applying Computational Intelligence: How to Create Value, Springer, Berlin.CrossRefGoogle Scholar
Kosko, B. (1993). Fuzzy Thinking - The New Science of Fuzzy Logic, Hyperion, New York.Google Scholar
Krohling, R.A. and dos Santos Coelho, L. (2006). Co-evolutionary Particle Swarm Optimisation Using Gaussian Distribution for Solving Constrained Optimisation Problems, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 36(6), 1407–1416.Google Scholar
Li, J-q. and Pan, Y-x. (2013). A Hybrid Discrete Particle Swarm Optimisation Algorithm for Solving Fuzzy Job Shop Scheduling Problem, International Journal of Advanced Manufacturing Technology, 66(1–4),583–596.Google Scholar
McCartney, B.L., George, J., Lee, B.K., Lindgren, M. and Neilson, F. (1998). Inland Navigation: Locks, Dams, and Channels, ASCE Manuals No 94, ASCE.Google Scholar
Mundy, R.A., Campbell, J.F. (2005). Management Systems for Inland Waterway Traffic Control, Volume II: Vessel Tracking for Managing Traffic on the Upper Mississippi River, Technical report, Final Report, Midwest Transportation Consortium, MTC Project 2004–003.Google Scholar
Negenborn, R.R., Lukszo, Z., Hellendoorn, H. (2010). Intelligent Infrastructures, Springer, Dordrecht.Google Scholar
Panigrahi, B.K., Shi, Y. and Lim, M-H. (2011), Handbook of Swarm Intelligence: Concepts, Principles and Applications (Adaptation, Learning, and Optimisation), Springer, Berlin.CrossRefGoogle Scholar
Partenscky, H.W. (1986). Inland Waterways: Ship Lock Installations, (Binnenverkehrswasserbau: Schleusenanlagen – in original), Springer, Berlin.Google Scholar
Pietrzykowski, Z. (2008). Ship's Fuzzy Domain – a Criterion for Navigational Safety in Narrow Fairways, Journal of Navigation, 61, 499–514.Google Scholar
Pietrzykowski, Z. and Uriasz, J. (2009). The Ship Domain – A Criterion of Navigational Safety Assessment in an Open Sea Area, Journal of Navigation, 62, 93–108.CrossRefGoogle Scholar
Plevris, V. and Papadrakakis, M., (2011). A Hybrid Particle Swarm—Gradient Algorithm for Global Structural Optimisation, Computer-Aided Civil and Infrastructure Engineering, 26(1), 48–68.Google Scholar
Praetorius, G., and Lützhöft, M. (2012). Decision support for vessel traffic service (VTS): user needs for dynamic risk management in the VTS. Work: A Journal of Prevention , Assessment and Rehabilitation, 41, 4866–4872.Google Scholar
Rapaić, M. and Kanović, Ž. (2009). Time-Varying PSO – Convergence Analysis, Convergence Related Parameterization and New Parameter Adjustment Schemes, Information Processing Letters, 109(1), 548–552.CrossRefGoogle Scholar
Ren, Y., Cao, G-y. and Zhu, X-j. (2006). Particle Swarm Optimisation based Predictive Control of Proton Exchange Membrane Fuel Cell (PEMFC), Journal of Zhejiang University Science A, 7(3), 458–462.Google Scholar
Sedki, A. and Ouazar, D. (2012). Hybrid Particle Swarm Optimisation and Differential Evolution for Optimal Design of Water Distribution Systems, Advanced Engineering Informatics, 26(3), 582–591.Google Scholar
Shafahi, Y. and Bagherian, M. (2013). A Customized Particle Swarm Method to Solve Highway Alignment Optimisation Problem, Computer-Aided Civil and Infrastructure Engineering, 28(1), 52–67.Google Scholar
Siddique, N. and Adeli, H. (2013). Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing, John Wiley and Sons.Google Scholar
Smith, L.D., Sweeney, D.C. II and Campbell, J.F. (2009). Simulation of Alternative Approaches to Relieving Congestion at Locks in a River Transportation System, Journal of the Operational Research Society, 60(4), 519–533.Google Scholar
Tapkan, P., Ozbakir, L. and Baykasoglu, A. (2013). Solving Fuzzy Multiple Objective Generalized Assignment Problems Directly via Bees Algorithm and Fuzzy Ranking, Expert Systems with Applications, 40(3), 892–898.CrossRefGoogle Scholar
Teodorović, D. and Vukadinović, K. (1998). Traffic Control and Transport Planning: A Fuzzy Sets and Neural Networks Approach, Kluwer Academia Publishers, Norwel, MA.CrossRefGoogle Scholar
Ting, C.J. and Schonfeld, P. (2001). Control Alternatives at a Waterway Lock, Journal of Waterway, Port, Coastal and Ocean Engineering, 127(2), 89–96.CrossRefGoogle Scholar
Tsou, M.C., Kao, S.L., & Su, C.M. (2010). Decision support from genetic algorithms for ship collision avoidance route planning and alerts. Journal of Navigation, 63(01), 167–182.Google Scholar
Wang, N., Meng, X., Xu, Q. & Wang, Z., (2009). A Unified Analytical Framework for Ship Domains, Journal of Navigation, 62, 643–655.Google Scholar
Wiegmans, B.W., & Konings, R. (2007). Strategies and innovations to improve the performance of barge transport. European journal of transport and infrastructure research EJTIR, 7(2), 145–161.Google Scholar
Willems, C. and Schmorak, N. (2010). River Information Services on the Way to Maturity, Proceedings on 32nd PIANC International Navigation Congress, Liverpool, United Kingdom.Google Scholar
Xu, G., Yang, Z-t. and Long, G-d. (2012). Multi-objective Optimisation of MIMO Plastic Injection Molding Process Conditions based on Particle Swarm Optimisation, International Journal of Advanced Manufacturing Technology, 58(5–8),521–531.Google Scholar
Zadeh, L.A. (1975). The Concept of a Linguistic Variable and its Application to Approximate reasoning, Information Sciences, 8, 199–249.CrossRefGoogle Scholar
Zhang, Y. and Ge, H. (2013). Freeway Travel Time Prediction Using Takagi-Sugeno-Kang Fuzzy Neural Network, Computer-Aided Civil and Infrastructure Engineering, 28(8), 594–603.Google Scholar