Browse > Article
http://dx.doi.org/10.5139/IJASS.2011.12.1.16

Nonlinear Model Predictive Control for Multiple UAVs Formation Using Passive Sensing  

Shin, Hyo-Sang (Korea Advanced Institute of Science and Technology)
Thak, Min-Jea (Korea Advanced Institute of Science and Technology)
Kim, Hyoun-Jin (Seoul National University)
Publication Information
International Journal of Aeronautical and Space Sciences / v.12, no.1, 2011 , pp. 16-23 More about this Journal
Abstract
In this paper, nonlinear model predictive control (NMPC) is addressed to develop formation guidance for multiple unmanned aerial vehicles. An NMPC algorithm predicts the behavior of a system over a receding time horizon, and the NMPC generates the optimal control commands for the horizon. The first input command is, then, applied to the system and this procedure repeats at each time step. The input constraint and state constraint for formation flight and inter-collision avoidance are considered in the proposed NMPC framework. The performance of NMPC for formation guidance critically degrades when there exists a communication failure. In order to address this problem, the modified optimal guidance law using only line-of-sight, relative distance, and own motion information is presented. If this information can be measured or estimated, the proposed formation guidance is sustainable with the communication failure. The performance of this approach is validated by numerical simulations.
Keywords
Unmanned air vehicles; Nonlinear model predictive control; Communication; Formation guidance; Collision avoidance;
Citations & Related Records

Times Cited By SCOPUS : 1
연도 인용수 순위
  • Reference
1 Ryoo, C. K., Kim, Y. H., and Tahk, M. J. (2005). An optimal formation guidance law for multiple unmanned aerial vehicles. 24th IASTED International Conference on Modeling, Identification, and Control, Innsbruck. pp. 445-450.
2 Sattigeri, R., Calise, A. J., and Evers, J. H. (2004). An adaptive vision-based approach to decentralized formation control. AIAA Guidance, Navigation, and Control Conference, Providence, RI. pp. 2575-2798.
3 Sutton, G. J. and Bitmead, R. R. (2000). Computational implementation of NMPC to nonlinear submarine. In F. Allgower and A. Zheng, eds. Nonlinear Model Predictive Control Vol 26. Boston, MA: Birkhauser Verlag. pp. 461-471.
4 Tahk, M. J., Park, C. S., and Ryoo, C. K. (2005). Line-of-sight guidance laws for formation flight. Journal of Guidance, Control, and Dynamics, 28, 708-716.   DOI   ScienceOn
5 Verma, A., Wu, C. N., and Castelli, V. (2003). Autonomous command and control system for UAV formation. AIAA Atmospheric Flight Mechanics Conference, Austin, TX.
6 Bhattacharya, R., Balas, G. J., Kaya, A., and Packard, A. (2001). Nonlinear receding horizon control of F-16 aircraft. Proceedings of the American Control Conference, Arlington, VA. pp. 518-522.
7 Bryson, A. E. and Ho, Y. C. (1975). Applied Optimal Control: Optimization, Estimation, and Control. Washington, DC: Hemisphere Publishing Corporation. pp. 212-245.
8 Das, A. K., Fierro, R., Kumar, V., Ostrowski, J. P., Spletzer, J., and Taylor, C. J. (2002). A vision-based formation control framework. IEEE Transactions on Robotics and Automation, 18, 813-825.   DOI   ScienceOn
9 Kim, H. J., Shim, D. H., and Sastry, S. (2002). Nonlinear model predictive tracking control for rotorcraft-based unmanned aerial vehicles. Proceedings of the American Control Conference, Anchorage, AK. pp. 3576-3581.
10 Kouvaritakis, B., Cannon, M., and Institution of Electrical Engineers. (2001). Nonlinear Predictive Control: Theory and Practice. London: Institution of Electrical Engineers. pp. 3-32.
11 Mayne, D. Q., Rawlings, J. B., Rao, C. V., and Scokaert, P. O. M. (2000). Constrained model predictive control: stability and optimality. Automatica, 36, 789-814.   DOI   ScienceOn
12 Michalska, H. and Mayne, D. Q. (1993). Robust receding horizon control of constrained nonlinear systems. IEEE Transactions on Automatic Control, 38, 1623-1633.   DOI   ScienceOn
13 Pachter, M., D’Azzo, J. J., and Proud, A. W. (2001). Tight formation flight control. Journal of Guidance, Control, and Dynamics, 24, 246-254.   DOI   ScienceOn