Open Access
Issue
MATEC Web Conf.
Volume 291, 2019
2019 The 3rd International Conference on Mechanical, System and Control Engineering (ICMSC 2019)
Article Number 01001
Number of page(s) 7
Section Control System
DOI https://doi.org/10.1051/matecconf/201929101001
Published online 28 August 2019
  1. M. Krstic, I. Kanellakopoulos, and P. V. Kokotovic, Nonlinear and Adaptive Control Design, New York: Wiley, (1995) [Google Scholar]
  2. Kanellakopoulos, P. V. Kokotovic, and A. S. Morse, Systematic design of adaptive controller for feedback linearizable system, IEEE Trans. Automat. Contr., 36: 1241–1253, (1991) [CrossRef] [Google Scholar]
  3. P. V. Kokotovic, The joy of feedback: nonlinear and adaptive, IEEE Contr. Syst. Mag., 12: 7–17, (1992) [CrossRef] [Google Scholar]
  4. M. Krstic and P. V. Kokotovic, Adaptive nonlinear design with controller-identifier eparation and swapping, IEEE Trans. Automat. Contr., 40: 426– 440, (1995) [CrossRef] [Google Scholar]
  5. C. Kwan and F. L. Lewis, Robust backstepping control of nonlinear systems using neural networks, IEEE Trans. Syst., Man, Cybern. A, 30: 753–765, (2000) [CrossRef] [Google Scholar]
  6. T. Knohl and H. Unbehauen, ANNNAC—extension of adaptive backstepping algorithm with artificial neural networks, Inst. Elect. Eng. Proc. Contr. Theory Appl., 147: 177–183, (2000) [CrossRef] [Google Scholar]
  7. D. M. Dawson, J. J. Carroll, and M. Schneider, Integrator backstepping control of a brush DC motor turning a robotic load, IEEE Trans. Contr. Syst. Technol., 2: 233–244, (1994) [CrossRef] [Google Scholar]
  8. S. R. Chu and R. Shoureshi, Neural-based adaptive nonlinear system identification, in Proc. Intelligent Control Systems, ASME Winter Annu. Meeting, DSC-45, (1992) [Google Scholar]
  9. B. Horn, D. Hush, and C. Abdallah, The state space recurrent neural network for robot identification, in Proc. Advanced Control Issues for Robot Manipulators, ASME Winter Annu. Meeting, DSC-39, (1992) [Google Scholar]
  10. F. C. Chen and H. K. Khalil, Adaptive control of nonlinear systems using neural networks, Int. J. Contr., 55 (6): 1299–1317, (1992) [CrossRef] [Google Scholar]
  11. K. S. Narendra, Adaptive control using neural networks, in Neural Networks for Control. Cambridge, MA: MIT Press, 115–142, (1991) [Google Scholar]
  12. T. Ozaki, T. Suzuki, T. Furuhashi, S. Okuma, and Y. Ushikawa, Trajectory control of robotic manipulators, IEEE Trans. Ind. Electron., 38: 195–202, (1991) [CrossRef] [Google Scholar]
  13. T. Zhang, S. S. Ge, C. C. Hang, Adaptive neural network control for strict-feedback nonlinear systems using backstepping design, Automatica, 36: 1835–1846, (2000) [CrossRef] [Google Scholar]
  14. D. Wang, J. Huang, Adaptive neural network control for a class of uncertain nonlinear systems in pure-feedback form, Automatica, 38: 1365–1372, (2002) [CrossRef] [Google Scholar]
  15. S. S. Ge and C. Wang, Adaptive Neural Control of Uncertain MIMO Nonlinear Systems, IEEE Trans. on Neural Networks, 15 (3): 674-692, (2004) [CrossRef] [Google Scholar]
  16. S. S. Ge, J. Zhang and T. H. Lee, Adaptive Neural Network Control for a Class of MIMO Nonlinear Systems with Disturbances in Discrete-Time, IEEE Trans. on Systems, Man, and Cybernetics – Part B: Cybernetics, 34 (4): 1630-1645, (2004) [CrossRef] [Google Scholar]
  17. D. Wang and J. Huang, Neural Network-based Adaptive Dynamic Surface Control for a class of Uncertain Nonlinear Systems in Strict-Feedback Form, IEEE Trans. on Neural Networks, 16 (1): 195-202, (2005) [CrossRef] [Google Scholar]
  18. C. F. Hsu, C. M. Lin and T. T. Lee, Wavelet Adaptive Backstepping Control for a Class of Nonlinear Systems, IEEE Trans. on Neural Networks, 17 (5): 1175-1183, (2006) [CrossRef] [Google Scholar]
  19. 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 Trans. on Cybernetics, 44 (12): 2626-2634, (2014) [CrossRef] [Google Scholar]
  20. Y. Pan, T. Sun, Y. Liu and H. Yu, Composite learning from adaptive backstepping neural network control, Neural Networks, 95: 134–142, (2017) [CrossRef] [Google Scholar]
  21. Y. Chen, J. Ren and C. Yi. Neural Networks for the Output Tracking-Control Problem of Nonlinear Strict-Feedback System, IEEE Access, 5: 26257–26266, (2017) [CrossRef] [Google Scholar]
  22. J. Yu, B. Chen, H. Yu, C. Lin, L. Zhao, Neural networks-based command filtering control of nonlinear systems with uncertain disturbance, Information Sciences, 426: 50–60, (2018) [CrossRef] [Google Scholar]
  23. Y. Yang and C. Zhou, Adaptive Fuzzy H∞ Stabilization for Strict-Feedback Canonical Nonlinear Systems Via Backstepping and Small-Gain Approach, IEEE Trans. on Fuzzy Systems, 13 (1): 104-114, (2005) [CrossRef] [Google Scholar]
  24. B. Chen and X. Liu, Fuzzy Approximate Disturbance Decoupling of MIMO Nonlinear Systems by Backstepping and Application to Chemical Processes, IEEE Trans. on Fuzzy Systems, 13 (6): 1-16, (2005) [CrossRef] [Google Scholar]
  25. P. Safi, and M. M. Entezari, Fuzzy Controller Design for a Novel Vehile Rollover Prevention System, Inter. J. of Machine Learning and Computing, 2 (5): 544-547, (2012) [Google Scholar]
  26. S. Tong, C. Li, Y. Li, Fuzzy adaptive observer backstepping control for MIMO nonlinear systems, Fuzzy Sets and Systems, (160): 2755–2775, (2009) [CrossRef] [Google Scholar]
  27. S. S. Ge, F. Hong and T. H. Lee, Adaptive Neural Control of Nonlinear Time-Delay Systems With Unknown Virtual Control Coefficients, IEEE Trans. on Systems, Man, and Cybernetics - Part B: Cybernetics, 34 (1): 499-516, (2004) [Google Scholar]
  28. F. Hong, S. S. Ge, and T. H. Lee, Practical Adaptive Neural Control of Nonlinear Systems With Unknown Time Delays, IEEE Trans on Systems, Man, and Cybernetics - Part B: Cybernetics, 35 (4): 849-854, (2005) [CrossRef] [Google Scholar]
  29. M. Wang, B. Chen and P. Shi, Adaptive Neural Control for a Class of Perturbed Strict-Feedback Nonlinear Time-Delay Systems, IEEE Trans. on Systems, Man, and Cybernetics - Part B: Cybernetics, 38 (3): 721-730, (2008) [CrossRef] [Google Scholar]
  30. S. C. Tong, Y. M. Li and H. G. Zhang, Adaptive Neural Network Decentralized Backstepping Output-Feedback Control for Nonlinear Large-Scale Systems with Time Delays, IEEE Trans. on Neural Networks, 22 (7): 1073-1086, (2011) [CrossRef] [Google Scholar]
  31. X. Shi, S. Xu, W. Chen, Y. Li and Z. Zhang, Adaptive neural control of switched nonstrict-feedback nonlinear systems with multiple time-varying delays, Journal of the Franklin Institute, 354: 8180–8199, (2017) [CrossRef] [Google Scholar]
  32. H. Wang, B. Chen, C. Lin and Y. Sun, Observer-based neural adaptive control for a class of MIMO delayed nonlinear systems with input nonlinearities, Neurocomputing, 275: 1988–1997, (2018) [CrossRef] [Google Scholar]
  33. Q. Zhou, P. Shi, S. Xu and H. Li, Observer-Based Adaptive Neural Network Control for Nonlinear Stochastic Systems With Time Delay, IEEE Trans. on Neural Networks and Learning Systems, 24 (1): 71-80, (2013) [CrossRef] [Google Scholar]
  34. N. Duan and H. F. Min, NN-based output tracking for more general stochastic nonlinear systems with unknown control coefficients, International Journal of Automation and Computing, 14 (3): 350-359, (2017) [CrossRef] [Google Scholar]
  35. Y. Yang, Z. Yu, S. Li and J. Sun, Adaptive neural output feedback control for stochastic nonlinear time-delay systems with input and output quantization, Neurocomputing, 000: 1–17, (2017) [CrossRef] [Google Scholar]
  36. G. Dong, Y. Li and Shuai Sui, Fault detection and fuzzy tolerant control for complex stochastic multivariable nonlinear systems, Neurocomputing, 275: 2392–2400, (2018) [CrossRef] [Google Scholar]
  37. W. Si, X. Dong and F. Yang, Adaptive neural prescribed performance control for a class of strict-feedback stochastic nonlinear systems with hysteresis input, Neurocomputing, 251: 35–44, (2017) [CrossRef] [Google Scholar]
  38. C. Xi, D. Zhai, X. Li, Q. Zhang, Decentralized adaptive delay-dependent neural network control for a class of large-scale interconnected nonlinear systems, Applied Mathematics and Computation, 311: 148–163, (2017) [CrossRef] [Google Scholar]
  39. M. K. Talkhoncheh, M. Shahrokhi and M. R. Askari, Observer-Based adaptive neural network controller for uncertain nonlinear systems with unknown control directions subject to input time delay and saturation, Information Sciences, 418: 717–737, (2017) [CrossRef] [Google Scholar]
  40. W. Si, X. Dong and F. Yang, Decentralized adaptive neural control for interconnected stochastic nonlinear delay-time systems with asymmetric saturation actuators and output constraints, Journal of the Franklin Institute, 355: 54–80, (2018) [CrossRef] [Google Scholar]
  41. C. Hua, K. Li, X. Guan, Decentralized event-triggered control for interconnected time-delay stochastic nonlinear systems using neural networks, Neurocomputing, 272: 270–278, (2018) [CrossRef] [Google Scholar]
  42. Z. Wang, J. Yuan, Y. Pan and D. Che, Adaptive neural control for high order Markovian jump nonlinear systems with unmodeled dynamics and dead zone inputs, Neurocomputing, 247: 62–72, (2017) [CrossRef] [Google Scholar]
  43. Y. Yang and D. Yue, Distributed tracking control of a class of multi-agent systems in non-affine pure-feedback form under a directed topology, IEEE/CAA Journal of Automatica Sinica, 5 (1): 169-180, (2017) [CrossRef] [Google Scholar]
  44. M. Chen, C. Jiang and Q. Wu, Backstepping control for a class of uncertain nonlinear systems with neural network, International Journal of nonlinear science, 3 (2): 137-143, (2007) [Google Scholar]
  45. S. S. Ge and J. Wang, Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems, IEEE Trans. on Neural Networks, 13 (6): 1409-1419, (2002) [CrossRef] [Google Scholar]
  46. Y. Li, S. Qiang, X. Zhuang and O. Kaynak, Robust and adaptive backstepping control for nonlinear systems using RBF neural networks, IEEE Trans. on Neural Networks, 15 (3): 693-701, (2004) [CrossRef] [Google Scholar]
  47. Y. Yang, G. Feng, and J. Ren, A combined backstepping and small-gain approach to robust adaptive fuzzy control for strict-feedback nonlinear systems, IEEE Trans. on Systems, Man, and Cybernetics - Part A: Systems and Humans, 34 (3): 406-420, (2004) [CrossRef] [Google Scholar]
  48. S. C. Tong, X. L. He and H. G. Zhang, A combined backstepping and small-gain approach to robust adaptive fuzzy output feedback control, IEEE Trans. on Fuzzy Systems, 17 (5): 1059-1069, (2009) [CrossRef] [Google Scholar]
  49. P. Kachroo and M. Tomizuka, Chattering Reduction and Error Convergence in the Sliding-Mode Control of a Class of Nonlinear Systems, IEEE Trans. on Auto. Contr., 41 (7): 1063-1068, (1996) [CrossRef] [Google Scholar]
  50. R. M. Sanner and J. J. E. Slotine, Gussian networks for direct adaptive control, IEEE Trans. on Neural Networks, 3: 837–863, (1992) [CrossRef] [Google Scholar]
  51. K. S. Narendra and A. M. Annaswamy, A new adaptive law for robust adaptation withour persistent excitation, IEEE Trans. Automat. Contr., 32: 134–145, (1987) [CrossRef] [Google Scholar]
  52. S. S. Ge and C. Wang, Direct adaptive NN control of a class of nonlinear systems, IEEE Trans. Neural Networks, 13 (1): 214-221, (2002) [CrossRef] [Google Scholar]

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