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Discrete Dynamics in Nature and Society
Volume 2016, Article ID 3460492, 8 pages
http://dx.doi.org/10.1155/2016/3460492
Research Article

Organization Learning Oriented Approach with Application to Discrete Flight Control

1School of Humanities, Economics and Law, Northwestern Polytechnical University, Xi’an 710072, China
2Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Received 25 December 2015; Accepted 28 February 2016

Academic Editor: Driss Boutat

Copyright © 2016 Lin Yu et al. 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. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002. View at Publisher · View at Google Scholar · View at Scopus
  2. M. Dorigo, M. Birattari, and T. Stützle, “Ant colony optimization artificial ants as a computational intelligence technique,” IEEE Computational Intelligence Magazine, vol. 1, no. 4, pp. 28–39, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Kennedy, “Particle swarm optimization,” in Encyclopedia of Machine Learning, pp. 760–766, Springer, 2010. View at Google Scholar
  4. B. Jarboui, M. Eddaly, and P. Siarry, “An estimation of distribution algorithm for minimizing the total flowtime in permutation flowshop scheduling problems,” Computers & Operations Research, vol. 36, no. 9, pp. 2638–2646, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. B. Xu, C. Yang, and Y. Pan, “Global neural dynamic surface tracking control of strict-feedback systems with application to hypersonic flight vehicle,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 10, pp. 2563–2575, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Na, X. Ren, and D. Zheng, “Adaptive control for nonlinear pure-feedback systems with high-order sliding mode observer,” IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 3, pp. 370–382, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. R. Lu, Y. Xu, A. Xue, and J. Zheng, “Networked control with state reset and quantized measurements: observer-based case,” IEEE Transactions on Industrial Electronics, vol. 60, no. 11, pp. 5206–5213, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. W. Chen, S. Hua, and S. S. Ge, “Consensus-based distributed cooperative learning control for a group of discrete-time nonlinear multi-agent systems using neural networks,” Automatica, vol. 50, no. 9, pp. 2254–2268, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. B.-S. Chen, C.-H. Lee, and Y.-C. Chang, “H tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach,” IEEE Transactions on Fuzzy Systems, vol. 4, no. 1, pp. 32–43, 1996. View at Publisher · View at Google Scholar
  10. Y.-J. Liu and S. Tong, “Adaptive fuzzy control for a class of unknown nonlinear dynamical systems,” Fuzzy Sets and Systems, vol. 263, pp. 49–70, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. M. Chen and G. Tao, “Adaptive fault-tolerant control of uncertain nonlinear large-scale systems with unknown dead zone,” IEEE Transactions on Cybernetics, no. 99, p. 1, 2015. View at Publisher · View at Google Scholar · View at Scopus
  12. B. Xu, Q. Zhang, and Y. Pan, “Neural network based dynamic surface control of hypersonic flight dynamics using small-gain theorem,” Neurocomputing, vol. 173, pp. 690–699, 2016. View at Google Scholar
  13. Y. Xu, R. Lu, H. Peng, K. Xie, and A. Xue, “Asynchronous dissipative state estimation for stochastic complex networks with quantized jumping coupling and uncertain measurements,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–10, 2015. View at Publisher · View at Google Scholar
  14. M. Chen and S. S. Ge, “Adaptive neural output feedback control of uncertain nonlinear systems with unknown hysteresis using disturbance observer,” IEEE Transactions on Industrial Electronics, vol. 62, no. 12, pp. 7706–7716, 2015. View at Publisher · View at Google Scholar
  15. P. A. Phan and T. Gale, “Two-mode adaptive fuzzy control with approximation error estimator,” IEEE Transactions on Fuzzy Systems, vol. 15, no. 5, pp. 943–955, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. S. S. Ge and C. Wang, “Direct adaptive NN control of a class of nonlinear systems,” IEEE Transactions on Neural Networks, vol. 13, no. 1, pp. 214–221, 2002. View at Publisher · View at Google Scholar · View at Scopus
  17. C. Yang, S. S. Ge, C. Xiang, T. Chai, and T. H. Lee, “Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach,” IEEE Transactions on Neural Networks, vol. 19, no. 11, pp. 1873–1886, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Chen and S. S. Ge, “Direct adaptive neural control for a class of uncertain nonaffine nonlinear systems based on disturbance observer,” IEEE Transactions on Cybernetics, vol. 43, no. 4, pp. 1213–1225, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. Y. J. Liu, Y. Gao, S. Tong, and Y. Li, “Fuzzy approximation-based adaptive backstepping optimal control for a class of nonlinear discrete-time systems with dead-zone,” IEEE Transactions on Fuzzy Systems, vol. 24, no. 1, pp. 16–28, 2016. View at Publisher · View at Google Scholar
  20. J.-H. Park, S.-H. Kim, and C.-J. Moon, “Adaptive neural control for strict-feedback nonlinear systems without backstepping,” IEEE Transactions on Neural Networks, vol. 20, no. 7, pp. 1204–1209, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. J. Na, Q. Chen, X. Ren, and Y. Guo, “Adaptive prescribed performance motion control of servo mechanisms with friction compensation,” IEEE Transactions on Industrial Electronics, vol. 61, no. 1, pp. 486–494, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. B. Xu, Y. Fan, and S. Zhang, “Minimal-learning-parameter technique based adaptive neural control of hypersonic flight dynamics without back-stepping,” Neurocomputing, vol. 164, no. 1-2, pp. 201–209, 2015. View at Publisher · View at Google Scholar · View at Scopus
  23. D. Liu and Q. Wei, “Finite-approximation-error-based optimal control approach for discrete-time nonlinear systems,” IEEE Transactions on Cybernetics, vol. 43, no. 2, pp. 779–789, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. D. Vrabie, O. Pastravanu, M. Abu-Khalaf, and F. Lewis, “Adaptive optimal control for continuous-time linear systems based on policy iteration,” Automatica, vol. 45, no. 2, pp. 477–484, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  25. Y. Gao and Y.-J. Liu, “Adaptive fuzzy optimal control using direct heuristic dynamic programming for chaotic discrete-time system,” Journal of Vibration and Control, vol. 22, no. 2, pp. 595–603, 2016. View at Publisher · View at Google Scholar · View at MathSciNet
  26. J. Na and G. Herrmann, “Online adaptive approximate optimal tracking control with simplified dual approximation structure for continuous-time unknown nonlinear systems,” IEEE/CAA Journal of Automatica Sinica, vol. 1, no. 4, pp. 412–422, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. B. Xu, C. Yang, and Z. Shi, “Reinforcement learning output feedback NN control using deterministic learning technique,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 3, pp. 635–641, 2014. View at Publisher · View at Google Scholar · View at Scopus
  28. Y.-J. Liu, L. Tang, S. Tong, C. L. P. Chen, and D.-J. Li, “Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 1, pp. 165–176, 2015. View at Publisher · View at Google Scholar · View at Scopus
  29. F. Lewis and D. Vrabie, “Reinforcement learning and adaptive dynamic programming for feedback control,” IEEE Circuits and Systems Magazine, vol. 9, no. 3, pp. 32–50, 2009. View at Google Scholar
  30. P. He and S. Jagannathan, “Reinforcement learning neural-network-based controller for nonlinear discrete-time systems with input constraints,” IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, vol. 37, no. 2, pp. 425–436, 2007. View at Publisher · View at Google Scholar · View at Scopus
  31. S. Jagannathan and F. Lewis, “Multilayer discrete-time neural-net controller with guaranteed performance,” IEEE Transactions on Neural Networks, vol. 7, no. 1, pp. 107–130, 1996. View at Google Scholar
  32. J. Sarangapani, Neural Network Control of Nonlinear Discrete-Time Systems, CRC Press Taylor & Francis Group, Boca Raton, Fla, USA, 2006.
  33. Y. J. Liu, S. Tong, D. J. Li, and Y. Gao, “Fuzzy adaptive control with state observer for a class of nonlinear discrete-time systems with input constraint,” IEEE Transactions on Fuzzy Systems, 2015. View at Publisher · View at Google Scholar
  34. R. F. Stengel, J. R. Broussard, and P. W. Berry, “Digital controllers for VTOL aircraft,” IEEE Transactions on Aerospace and Electronic Systems, vol. AES-14, no. 1, pp. 54–63, 1978. View at Publisher · View at Google Scholar · View at Scopus
  35. A. Chaudhuri and M. S. Bhat, “Output feedback-based discrete-time sliding-mode controller design for model aircraft,” Journal of Guidance, Control, and Dynamics, vol. 28, no. 1, pp. 177–181, 2005. View at Publisher · View at Google Scholar · View at Scopus
  36. B. Xu and Y. Zhang, “Neural discrete back-stepping control of hypersonic flight vehicle with equivalent prediction model,” Neurocomputing, vol. 154, pp. 337–346, 2015. View at Publisher · View at Google Scholar · View at Scopus
  37. F. R. Chavez and D. K. Schmidt, “Analytical aeropropulsive-aeroelastic hypersonic-vehicle model with dynamic analysis,” Journal of Guidance, Control, and Dynamics, vol. 17, no. 6, pp. 1308–1319, 1994. View at Publisher · View at Google Scholar · View at Scopus
  38. D. K. Schmidt, “Optimum mission performance and multivariable flight guidance for airbreathing launch vehicles,” Journal of Guidance, Control, and Dynamics, vol. 20, no. 6, pp. 1157–1164, 1997. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  39. D. O. Sigthorsson, P. Jankovsky, A. Serrani, S. Yurkovich, M. A. Bolender, and D. B. Doman, “Robust linear output feedback control of an airbreathing hypersonic vehicle,” Journal of Guidance, Control, and Dynamics, vol. 31, no. 4, pp. 1052–1066, 2008. View at Publisher · View at Google Scholar · View at Scopus
  40. L. Fiorentini, A. Serrani, M. A. Bolender, and D. B. Doman, “Robust nonlinear sequential loop closure control design for an air-breathing hypersonic vehicle model,” in Proceedings of the American Control Conference (ACC '08), pp. 3458–3463, IEEE, Seattle, Wash, USA, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  41. H. Xu, M. D. Mirmirani, and P. A. Ioannou, “Adaptive sliding mode control design for a hypersonic flight vehicle,” Journal of Guidance, Control, and Dynamics, vol. 27, no. 5, pp. 829–838, 2004. View at Publisher · View at Google Scholar · View at Scopus
  42. B. Xu, “Robust adaptive neural control of flexible hypersonic flight vehicle with dead-zone input nonlinearity,” Nonlinear Dynamics, vol. 80, no. 3, pp. 1509–1520, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  43. S. Wang, Y. Zhang, Y. Jin, and Y. Zhang, “Neural control of hypersonic flight dynamics with actuator fault and constraint,” Science China Information Sciences, vol. 58, no. 7, pp. 1–10, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  44. B. Xu, Y. Guo, Y. Yuan, Y. Fan, and D. Wang, “Fault-tolerant control using command-filtered adaptive back-stepping technique: application to hypersonic longitudinal flight dynamics,” International Journal of Adaptive Control and Signal Processing, 2015. View at Publisher · View at Google Scholar · View at Scopus
  45. L. Fiorentini and A. Serrani, “Adaptive restricted trajectory tracking for a non-minimum phase hypersonic vehicle model,” Automatica, vol. 48, no. 7, pp. 1248–1261, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  46. B. Xu and Z. Shi, “An overview on flight dynamics and control approaches for hypersonic vehicles,” Science China Information Sciences, vol. 58, no. 7, pp. 1–19, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  47. W. Chen, S. Hua, and H. Zhang, “Consensus-based distributed cooperative learning from closed-loop neural control systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 2, pp. 331–345, 2015. View at Publisher · View at Google Scholar · View at Scopus
  48. W. Chen and W. Ren, “Event-triggered zero-gradient-sum distributed consensus optimization over directed networks,” Automatica, vol. 65, pp. 90–97, 2016. View at Publisher · View at Google Scholar · View at MathSciNet
  49. B. Xu, Z. Shi, C. Yang, and F. Sun, “Composite neural dynamic surface control of a class of uncertain nonlinear systems in strict-feedback form,” IEEE Transactions on Cybernetics, vol. 44, no. 12, pp. 2626–2634, 2014. View at Publisher · View at Google Scholar · View at Scopus