Multirate Neural Control for AUV's Increased Situational Awareness during Diving Tasks Using Stochastic Model
This paper focuses on a critical component of the situational awareness (SA), the neural control of depth flight of an autonomous underwater vehicle (AUV). Constant depth flight is a challenging but important task for AUVs to achieve high level of autonomy under adverse conditions. With the SA strategy, we proposed a multirate neural control of an AUV trajectory for a nontrivial mid-small size AUV “r2D4" stochastic model. This control system has been demonstrated and evaluated by simulation of diving maneuvers using software package Simulink. From the simulation results it can be seen that the chosen AUV model is stable in the presence of noises, and also can be concluded that the proposed research technique will be useful for fast SA of similar AUV systems in real-time search-and-rescue operations.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1072257Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1200
 M. R. Endsley, "Toward a theory of situation awareness in dynamic systems," Human Factors, vol. 37, pp. 32-64, March 1995.
 J. Gorman, N. Cooke, and J. Winner, "Measuring team situation awareness in decentralized command and control environments," Ergonomics, vol. 49, pp. 1312-1325, October 2006.
 Interim Brigade Combat Team Newsletter.
[Online]. Available: http://www.globalsecurity.org/military/library/ report/call/call_01-18_ toc.htm
 I. Astrov and A. Pedai, "Enhancing situational awareness by means of hybrid adaptive neural control of vertical flight in unmanned helicopter," in Proc. International Conf. Control, Automation and Systems 2008, Seoul, Korea, 2008, pp. 329-332.
 T. Ura, T. Obara, K. Nagahashi, K. Kim, Y. Oyabu, T. Sakamaki, A. Asada, and H. Koyama, "Introduction to an AUV "r2D4"and its Kuroshima Knoll survey mission," in Proc. Conf. OCEANS -04. MTTS/IEEE TECHNO-OCEAN -04, Kobe, Japan, 2004, pp. 840-845.
 J. H. Li and P. M. Lee, "Design of an adaptive nonlinear controller for depth control of an autonomous underwater vehicle," Ocean Engineering, vol. 32, pp. 2165-2181, December 2005.
 I. Astrov, A. Pedai, and E. R├╝stern, "Simulation of two-rate adaptive hybrid control with neural and neuro-fuzzy networks for stochastic model of missile autopilot," in Proc. 5th World Congress on Intelligent Control and Automation, Hangzhou, China, 2004, pp. 2603-2607.
 K. S. Narendra and S. Mukhopadhyay, "Adaptive control using neural networks and approximate models," IEEE Trans. Neural Networks., vol. 8, pp. 475-485, May 1997.