Abstract
We applied adaptive neural-based fuzzy inference system (ANFIS) approach to the process control of ship automation manipulating systems. This paper studied the design of ANFIS controller for ship steering control system. Using BP algorithm to the learning of premise parameters, while least square algorithm to the learning of consequent parameters, we applied ANFIS approach to ship autopilot. To perform the ship task of steering a ship effectively, a ship autopilot system based on ANFIS approach is designed for various outer surroundings at sea in performing course keeping, course-changing more robustly. The simulating results by Matlab indicate that the performance of ANFIS controller is valuable and easy to implement.
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References
Im, N.K., Hasegawa, K.: A Study on Automatic Ship Berthing using Parallel Neural Controller. J. Kansai Soc. N.A. 236, 65–70 (2001)
Nguyen, P.H.: A Study on the Automatic Ship Control Based on Adaptive Neural Networks. PhD. Thesis, Graduate School of Korea Maritime University (2007)
Tamaru, H., Hagiwara, H., Yoshida, H., Tasaki, T., Miyabe, H.: Development of Automatic Berthing System for Kaisho Maru and its Performance Evaluation. The Journal of Japan Institute of Navigation 113, 157–164 (2005)
Zhang, Y., Hearn, G.E., Sen, P.: Neural Network Approaches to a Class of Ship Control Problems (Part I, II). In: Eleventh Ship Control Systems Symposium, vol. 1, pp. 115–150 (1997)
Sutton, R., Craven, P.J.: The ANFIS Approach Applied to AUV Autopilot Design. Neural Computing and Applications 7, 131–140 (1998)
Hardy, R.L.: Multiquadric Equations of Topography and Other Irregular Surfaces. Journal of Geophysical Research 76, 1905–1915 (1971)
Jang, J.S., Neuro-Fuzzy, R.: Modeling: Architecture, Analyses and Applications. PhD Thesis, University of California, Berkeley (1992)
Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and its Applications to Modelling and Control. IEEE Transactions on Systems, Man and Cybernetics 15, 116–132 (1985)
Jang, J.S., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing. Prentice-Hall, New Jersey (1997)
Han, X.L., Tong, S.C.: A Hybrid Adaptive Fuzzy Control for a Class of Nonlinear MIMO systems. IEEE Transactions on Fuzzy Systems 11, 24–34 (2003)
Li, C., Lee, C.Y.: Self-organizing Neuro-fuzzy System for Control of Unknown Plants. IEEE Transactions on Fuzzy Systems 11, 135–150 (2003)
Wang, C.H., Liu, H.L., Lin, T.C.: Direct Adaptive Fuzzy-neural Control with State Observer and Supervisory Controller for Unknown Nonlinear Dynamical Systems. IEEE Transactions on Fuzzy Systems 10, 39–49 (2002)
Fernández, F., Gutiérrez, J.: A Takagi–Sugeno Model with Fuzzy Inputs Viewed from Multidimensional Interval Analysis. Fuzzy Sets and Systems 135, 39–61 (2003)
Nomura, H., Hayashi, I., et al.: A Learning Method of Fuzzy Inference Rules by Decent Method. In: IEEE Internet. Conf. on Fuzzy Systems. IEEE Press, New York (1992)
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Minghui, W., Yongquan, Y., Wei, L. (2009). Adaptive Neural-Based Fuzzy Inference System Approach Applied to Steering Control. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_136
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DOI: https://doi.org/10.1007/978-3-642-01510-6_136
Publisher Name: Springer, Berlin, Heidelberg
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