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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 472815, 9 pages
http://dx.doi.org/10.1155/2014/472815
Research Article

An Adaptive Nonlinear Control for Gyro Stabilized Platform Based on Neural Networks and Disturbance Observer

Fundamental Science on Novel Inertial Instrument & Navigation System Technology Laboratory, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

Received 3 July 2014; Revised 30 October 2014; Accepted 10 November 2014; Published 25 November 2014

Academic Editor: Vu Ngoc Phat

Copyright © 2014 Jiancheng Fang and Rui Yin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. M. Hilkert, “Inertially stabilized platform technology: concepts and principles,” IEEE Control Systems Magazine, vol. 28, no. 1, pp. 26–46, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  2. M. K. Masten, “Inertially stabilized platforms for optical imaging systems,” IEEE Control Systems, vol. 28, no. 1, pp. 47–64, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. H. G. Wang and T. C. Williams, “Strategic inertial navigation systems—high-accuracy inertially stabilized platforms for hostile environments,” IEEE Control Systems, vol. 28, no. 1, pp. 65–85, 2008. View at Publisher · View at Google Scholar
  4. J. L. Miller, S. Way, B. Ellison, and C. Archer, “Design challenges regarding high-definition electro-optic/infrared stabilized imaging systems,” Optical Engineering, vol. 52, no. 6, Article ID 061310, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. R. Miller, G. Mooty, and J. M. Hilkert, “Gimbal system configurations and line-of-sight control techniques for small UAV applications,” in Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications X, vol. 8713 of Proceedings of SPIE, pp. 1–15, 2013. View at Publisher · View at Google Scholar
  6. J. Hilkert and D. L. Amil, “Structural effects and techniques in precision pointing and tracking systems—a tutorial overview,” in Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, vol. 7696 of Proceedings of SPIE, 76961C, p. 1-12, 2010.
  7. J. M. Hilkert and B. Pautler, “A reduced-order disturbance observer applied to inertially stabilized Line-of-Sight control,” in 25th Acquisition, Tracking, Pointing, and Laser Systems Technologies, vol. 8052 of Proceedings of SPIE, p. 80520H, Orlando, Fla, USA, April 2011. View at Publisher · View at Google Scholar
  8. C. Wu and Z. Lin, “Disturbance observer based control system design for inertially stabilized platform,” in Electro-Optical Remote Sensing, Photonic Technologies, and Applications VI, 85420T, vol. 8542 of Proceedings of SPIE, Edinburgh, UK, September 2012. View at Publisher · View at Google Scholar
  9. A. Li, C. Hong, S. Zhang, and C. Yuan, “High-precision stabilization control for a floated inertial platform,” in Proceedings of the 25th Chinese Control and Decision Conference (CCDC '13), pp. 1193–1199, May 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. Hu, Y. Cao, and S. Zhang, “Design of sliding mode control with disturbance observers for inertial platform,” in Proceedings of the 25th Chinese Control and Decision Conference (CCDC '13), pp. 4652–4656, May 2013. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Mondal, S. Sadhu, and A. Banerjee, “Platform motion disturbances attenuation in a missile seeker subsystem using Internal Model Control,” in Proceedings of the International Conference on Control, Automation, Robotics and Embedded Systems (CARE ’13), pp. 1–4, Jabalpur, India, December 2013. View at Publisher · View at Google Scholar
  12. Z. Yang, J. Wu, and J. Mei, “Motor-mechanism dynamic model based neural network optimized computed torque control of a high speed parallel manipulator,” Mechatronics, vol. 17, no. 7, pp. 381–390, 2007. View at Publisher · View at Google Scholar · View at Scopus
  13. X. Sun, L. Chen, Z. Yang, and H. Zhu, “Speed-sensorless vector control of a bearingless induction motor with artificial neural network inverse speed observer,” IEEE/ASME Transactions on Mechatronics, vol. 18, no. 4, pp. 1357–1366, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. J.-F. Couchot, K. Deschinkel, and M. Salomon, “Active MEMS-based flow control using artificial neural network,” Mechatronics, vol. 23, no. 7, pp. 898–905, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. X. Ren, C. Y. Lai, V. Venkataramanan, F. L. Lewis, S. S. Ge, and T. Liew, “Feedforward control based on neural networks for disturbance rejection in hard disk drives,” IET Control Theory & Applications, vol. 3, no. 4, pp. 411–418, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  16. K. K. Ahn and H. P. H. Anh, “Design and implementation of an adaptive recurrent neural networks (ARNN) controller of the pneumatic artificial muscle (PAM) manipulator,” Mechatronics, vol. 19, no. 6, pp. 816–828, 2009. View at Publisher · View at Google Scholar · View at Scopus