Tzung-hang Lee This email address is being protected from spambots. You need JavaScript enabled to view it.1 , Yusong Cao2 and Yen-mi Lin1
1Department of Mechanical & Electro-Mechanical Engineering Tamkang University Tamsui, Taiwan 251, R.O.C. 2School of Naval Architecture and Marine Engineering University of New Orleans New Orleans, LA, U.S.A. 70115
Received: May 7, 2002 Accepted: July 26, 2002 Publication Date: September 1, 2002
In this paper, a fuzzy logic controller for dynamic positioning of floating structures in deep water is presented. The core of the fuzzy controller is a set of fuzzy associative memory (FAM) rules that correlate each group of fuzzy control input sets to a fuzzy control output set. A FAM rule is a logical if-then type statement based on one’s sense of realism and experience or can be provided by an expert operator. The design of the fuzzy controller is very simple and does not require mathematical modeling of the complicated nonlinear system based on first principles. The fuzzy controller uses measured structure heading, yaw rate, distance and velocity of the structure relative to the desired position (location and heading) to generate the control outputs to bring the structure to and maintain it in the desired position. The control outputs include the rudder angle, propeller thrust and lateral bow thrust. The effectiveness and robustness of the fuzzy controller are demonstrated through numerical time-domain simulations of the dynamic positioning of a drill ship of Mariner Class hull with use of nonlinear ship equations of motions.
Keywords: FAM, Fuzzy Logic Controller, Dynamic Positioning, Time Domain
REFERENCES
[1] CMPT (1998), “Floating Structures: a Guide for Design and Analysis” (CMPT).
[2] Kosko, B., Neural Networks Fuzzy Systems – a Dynamical Systems Approach to Machine Intelligence, Prentice-Hall, Englewood, N.J., U.S.A., pp. 171-250 (1992).
[3] Ishii, K., Fujii, T. and Ura, T., “A Quick Adaptive Method in a Neural Network Based Control System for AUVs,” Proceedings Symposium on Autonomous Underwater Vehicle Technology, Cambridge, MA, U.S.A. (1994).
[4] Zhang, Y., Hearn, G. E. and Sen, P., “Neural Network Approaches to a Class of Ship Control. Part I: Theoretical Design, and Part II: Simulation Studies,” Proceedings 11th Ship Control Systems Symposium, University of Southampton, U.K. (1997).
[5] Gu, M. X., Pao, Y. H. and Yip, P. P. C., “Neural-net Computing for Real-time Control of a Ship’s Dynamic Positioning at Sea,” Computer Engineering Practice, Vol. 1 pp. 305-314 (1993).
[6] Li, D. and Gu, M. X., “Dynamic Positioning of Ships Using a Planned Neural Network Controller,” Journal of Ship Research, Vol. 40, pp. 279-291 (1996).
[7] Cao, Y., Zhou, Z. and Vorus, W. S., “Application of a Neural Network Predictor/controller to Dynamic Positioning of Offshore Structures,” Proceedings Dynamic Positioning Conference (DP 2000), The Society of Marine Technology, Houston, TX, U.S.A. (2000)
[8] DeBitetto, P. A., “Fuzzy Logic for Depth Control of Unmanned Undersea Vehicles,” Proceedings. Symposium on Autonomous Underwater Vehicle Technology, Cambridge, MA, U.S.A. (1994).
[9] Robert, G. N., “Approaches to Fuzzy Autopilot Design Optimization,” Proceedings IFAC Conference on Maneuvering and Control of Marine Crafts, Brijuni, Croatia (1997).
[10] Fang, M-C. and Chiou, S-C., “SWATH Ship Motion Simulation Based on a Self-tuning Fuzzy Control,” Journal of Ship Research, Vol. 44, pp. 108-119 (2000).
[11] Parsons, M. G., Chubb A. C. and Cao Y., “An Assessment of Fuzzy Logic Structure Path Control”, IEEE Journal of Oceanic Engineering, Vol. 20, pp. 117-129 (1995).
[12] Cao, Y. and Lee, T., “Maneuvering of Surface Structures Using a Fuzzy Logical Controller,” Journal of Ship Research, (Accepted) (2001).
[13] Lewis E., “Principles of Naval Architecture,” Society of Naval Architects and Marine Engineers, Vol. 3, pp. 71-83 (1989).
We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.
