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Depth Controls of an Autonomous Underwater Vehicle by Neurocontrollers for Enhanced Situational Awareness

Authors: Igor Astrov, Andrus Pedai

Abstract:

This paper focuses on a critical component of the situational awareness (SA), the neural control of autonomous constant depth flight of an autonomous underwater vehicle (AUV). Autonomous constant depth flight is a challenging but important task for AUVs to achieve high level of autonomy under adverse conditions. The fundamental requirement for constant depth flight is the knowledge of the depth, and a properly designed controller to govern the process. The AUV, named VORAM, is used as a model for the verification of the proposed hybrid control algorithm. Three neural network controllers, named NARMA-L2 controllers, are designed for fast and stable diving maneuvers of chosen AUV model. This hybrid control strategy for chosen AUV model has been verified by simulation of diving maneuvers using software package Simulink and demonstrated good performance for fast SA in real-time searchand- rescue operations.

Keywords: Autonomous underwater vehicles, depth control, neurocontrollers, situational awareness.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1078923

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References:


[1] M. R. Endsley, "Toward a theory of situation awareness in dynamic systems," Human Factors, vol. 37, pp. 32-64, March 1995.
[2] 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.
[3] Interim Brigade Combat Team Newsletter. (Online). Available: http://www.globalsecurity.org/military/library/ report/call/call_01-18_ toc.htm
[4] I. Astrov and A. Pedai, "Enhancing situational awareness by means of hybrid adaptive neural control of vertical flight in unmanned helicopter," in Proc. Int. Conf. Control, Automation and Systems, Seoul, Korea, October 14-17, 2008, pp. 329-332.
[5] P. M. Lee, S. W. Hong, Y. K. Lim, C. M. Lee, B. H. Jeon, and J. W. Park, "Discrete-time quasi-sliding mode control of an autonomous underwater vehicle," IEEE J. Oceanic Eng., vol. 24, pp. 388-395, July 1999.
[6] 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.