Neural Network Motion Control of VTAV by NARMA-L2 Controller for Enhanced Situational Awareness

Authors: Igor Astrov, Natalya Berezovski

Abstract:

This paper focuses on a critical component of the situational awareness (SA), the control of autonomous vertical flight for vectored thrust aerial vehicle (VTAV). With the SA strategy, we proposed a neural network motion control procedure to address the dynamics variation and performance requirement difference of flight trajectory for a VTAV. This control strategy with using of NARMAL2 neurocontroller for chosen model of VTAV has been verified by simulation of take-off and forward maneuvers using software package Simulink and demonstrated good performance for fast stabilization of motors, consequently, fast SA with economy in energy can be asserted during search-and-rescue operations.

Keywords: NARMA-L2 neurocontroller, situational awareness, vectored thrust aerial vehicle.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1338331

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] S. D. Prior, S. T. Shen, A. S. White, S. Odedra, M. Karamanoglu, M. A. Erbil, and T. Foran, “Development of a novel platform for greater situational awareness in the urban military terrain,” in Proc. 8th International Conf. Engineering Psychology and Cognitive Ergonomics, San Diego, USA, 2009, pp. 120-125.
[5] I. Astrov and A. Pedai, “Control of hovering manoeuvres in unmanned helicopter for enhanced situational awareness,” in Proc. International Conf. Industrial Mechatronics and Automation, Chengdu, China, 2009, pp. 143-146.
[6] W. Yuan, H. Jayakody, and J. Katupitiya, “Dynamic modelling and analysis of a vectored thrust aerial vehicle,” in Proc. Australasian Conf. Robotics and Automation, Wellington, New Zealand, 2012, pp. 1-8.
[7] 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.