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JRM Vol.28 No.6 pp. 928-935
doi: 10.20965/jrm.2016.p0928
(2016)

Paper:

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Design of the Nonlinear Structure Adaptive Model Inversion Flight Control System

Hao Long*,** and Yi-Nung Chung**

*College of Automation, Beijing Union University
No.97 Beisihuan East Road, Chao Yang District, Beijing, China

**Department of Electrical Engineering, National Changhua University of Education
No.2, Shi-Da Road, Changhua, Taiwan

Received:
April 22, 2016
Accepted:
October 19, 2016
Published:
December 20, 2016
Keywords:
nonlinear system, structure adaptive model inversion control, flight control
Abstract

Design of the Nonlinear Structure Adaptive Model Inversion Flight Control System

Design of the nonlinear SAMI flight control system

The advanced aircraft has those technological characteristics: stealth, supersonic cruise, super maneuverability, multi-target attack, multi-role, high load, long range cruise, integrated avionics, short takeoffs and vertical landings. These characteristics need multi-subsystem integration in the advanced aircraft. In order to meet the need of advance aircraft, the nonlinear control system is necessary. In this paper, the nonlinear structure adaptive model inversion system was first used for the nonlinear control problem of super-maneuver aircraft. The sufficient and necessary condition of the control law was analyzed. Finally the results of the height-angle maneuver simulation show that the designed control system has good performance.

Cite this article as:
H. Long and Y. Chung, “Design of the Nonlinear Structure Adaptive Model Inversion Flight Control System,” J. Robot. Mechatron., Vol.28, No.6, pp. 928-935, 2016.
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References
  1. [1] R.-G. Zhu and C.-J. Jiang, “Ect study on dynamic inversion control and simulation of supermaneuverable flight of the new generation fighter,” Acta Aeronauticaet Astronautica Sinica, Vol.24, pp. 242-245, 2003.
  2. [2] H. Kino and S. Kawamura, “Mechanism and Control of Parallel-wire Driven System,” J. of Robotics and Mechachonics, Vol.27, No.6, pp. 599-607, 2015.
  3. [3] J.-Q. Zhu and S.-F. Guo, “Neural net based adaptive reconfigurable control for a super maneuverable aircraft,” Acta Aeronautica Et Astronautica Sinica Press, Vol.24, pp. 246-250, 2003.
  4. [4] Z.-G. Xiong, X.-X. Sun, and M.-Q. Hu, “Active disturbances rejection control law design of aircraft super-maneuverable flight and simulations,” J. of system simulation, Vol.18, pp. 2222-2226, 2006.
  5. [5] H. Long, S.-J. Song, and H.-Q. Su, “The Study of Horizon Optimal Control for a Class of Affine Nonlinear System,” The 7th World Congress on Intelligent Control and Automation, Chongqing, China, pp. 2008-2995, June 2008.
  6. [6] K. Kurashiki, M. Aguilar, and S. Soontornvanichkit, “Visual Navigation of A Wheeled Mobile Robot Using Front Image In Semi-Structured Environment,” J. of Robotics and Mechatronics, Vol.27, No.4, pp. 392-400, 2015.
  7. [7] T.-F. Zhu, M. Li, and J.-H. Deng, “Nonlinear flight control system based on back stepping theory and supermaneuver,” Acta aeronautica et astronautica sinica press, Vol.26, pp. 430-433, 2005.
  8. [8] T.-F. Zhu, Z.-C. Liu, and Z.-X. Yao, “Based on synthesized nonlinear control method,” J. of system simulation, Vol.21. pp. 521-523, 2009.
  9. [9] S. Shujie, “The research of integrated vehicle management system,” Ph.D. thesis, Xi’an China, Northwestern Polytechnical University, 2007.
  10. [10] T. Takubo, K. Kominami, K. Ohara, Y. Mae, and T. Arai, “Collision Avoidance Using Contact Information With Multiple Objects By Multi-leg Robot,” J. of Robotics and Mechatronics, Vol.28, No.1, pp. 17-30, 2016.
  11. [11] C. Ma and L. X. Wang, “Flying-wing aircraft control allocation,” 47th AIAA Aerospace Sciences Meeting, Curran Associates, Florida, pp. 1-22, 2009.
  12. [12] L. A. Weitz, J. E. Hurtado, and A. J. Sinclair, “Decentralized cooperative-control design for multivehicle formations,” J. of Guidance, Control, and Dynamics, Vol.31, No.4, pp. 970-979, 2008.
  13. [13] D. Li, N. Hovakimyan, and C. Cao, “Positive invariance set of L1 adaptive controller in the presence of input saturation,” AIAA Guidance, Navigation, and Control Conf., Toronto, Ontario, Canada, 7535, 2010.
  14. [14] W. Dong, “Robust formation control of multiple wheeled mobile robots,” J. of Intelligent and Robotic Systems, Vol.62, No.3, pp. 547-565, 2011.
  15. [15] D. J. Bennet and C. R. McInnes, “Autonomous three-dimensional formation flight for a swarm of unmanned aerial vehicles,” J. of Guidance, Control and Dynamics, Vol.34, No.6, pp. 1899-1908, 2011.
  16. [16] P. Lin and Y. Jia, “Distributed rotating formation control of multi-agent systems,” Systems and Control Letters, Vol.59, pp. 587-595, 2010.
  17. [17] Y.-X. Zhang and Y.-Z. Chen, “Average dwell-time condition for consensus of linear multi-agent systems with time-varying delay and switching directed communication topologies protocol,” Control and Decision, Vol.31, No.2, pp. 249-354, 2016.
  18. [18] D. J. R. Jose, P. C. J. Humberto, Z. Zamudio et al., “Comparison of Two Quadrotor Dynamic Models,” Latin America Trans. IEEE, Vol.12, No.4, pp. 531-537, 2014.
  19. [19] L. Garcí-Delgado, A. Dzul, V. Santibá’nez et al., “Quad-rotors Formation Based on Potential Functions with Obstacle Avoidance,” IET Control Theory & Applications, Vol.6, No.12, pp. 1787-1802, 2012.
  20. [20] A. C. Satici and H. Poonawala, “Spong M W. Robust Optimal Control of Quadrotor UAVs,” Access IEEE, Vol.1, pp. 79-93, 2013.
  21. [21] B. L. Chang, “A dynamic virtual structure formation control for fixed-wing UAVs,” 2011 9th IEEE Int. Conf. on Control and Automation, pp. 627-632, 2011.
  22. [22] D. Galzi and Y. Shtessel, “Unmanned rotorcraft tight formation flight control using sliding mode control driven by sliding mode disturbance observers,” AIAA Guidance, Navigation and Control Conf. and Exhibit, 2008.

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Last updated on Dec. 10, 2019