Approximation-based adaptive tracking control of nonlinear pure-feedback systems with time-varying output constraints

International Journal of Control, Automation and Systems

Volumn 13, Issue 2, 2015, Pages 257-265

  Kim, Bong Su ^a   Yoo, Sung Jin ^a  

^a Chung Ang University   (South Korea)

Author keywords

Dynamic surface design; function approximation technique; pure feedback systems; time varying output constraint

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; CLOSED LOOP CONTROL SYSTEMS; CLOSED LOOP SYSTEMS; CONTROL NONLINEARITIES; CONTROLLERS; FEEDBACK CONTROL; LYAPUNOV FUNCTIONS; NONLINEAR FEEDBACK; TIME VARYING NETWORKS;

ADAPTIVE DYNAMIC SURFACES; ADAPTIVE NEURAL NETWORK CONTROL; ADAPTIVE TRACKING CONTROL; DYNAMIC SURFACE DESIGNS; FUNCTION APPROXIMATION TECHNIQUES; PURE-FEEDBACK SYSTEM; SEMI-GLOBALLY UNIFORMLY ULTIMATELY BOUNDED; TIME-VARYING OUTPUT;

TIME VARYING CONTROL SYSTEMS;

EID: 84939962620     PISSN: 15986446     EISSN: 20054092     Source Type: Journal    
DOI: 10.1007/s12555-014-0084-6     Document Type: Article

References (25)

1. M. Krstic, I. Kanellakopoulos, and P. Kokotovic, Nonlinear and Adaptive Control Design, Hoboken, Wiley, NJ, 1995.
2. D. Swaroop, J. K. Hedrick, P. P. Yip, and J. C. Gerdes, “Dynamic surface control for a class of nonlinear systems,” IEEE Trans. on Automatic Control, Vol. 45, No. 10, pp. 1893–1899, 2000.
3. S. S. Ge and C. Wang, “Adaptive NN control of uncertain nonlinear pure-feedback systems,” Automatica, Vol. 38, No. 4, pp. 671–682, 2002.
4. D. Wang and J. Huang, “Adaptive neural network control for a class of uncertain nonlinear systems in pure-feedback form,” Automatica, Vol. 38, No. 8, pp. 1365–1372, 2002.
5. T. P. Zhang and S. S. Ge, “Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form,” Automatica, Vol. 44, No. 7, pp. 1895–1903, 2008.
6. C. Wang, D. J. Hill, S. S. Ge, and G. Chen, “An ISS-modular approach for adaptive neural control of pure-feedback systems,” Automatica, Vol. 42, No. 5, pp. 723–731, 2006.
7. M. Wang, X. Liu, and P. Shi, “Adaptive neural control of pure-feedback nonlinear time-delay systems via dynamic surface technique,” IEEE Trans. Systems, Man, and Cybernetics, Vol. 41, No. 6, pp. 1681–1692, 2011.
8. T. P. Zhang, H. Wen, and Q. Zhu, “Adaptive fuzzy control of nonlinear systems in pure feedback form based on input-to-state stability,” IEEE Trans. on Fuzzy Systems, Vol. 18, No. 1, pp. 80–93, 2010.
9. G. Sun, D. Wang, X. Li, and Z, Peng, “A DSC approach to adaptive neural network tracking control for pure-feedback nonlinear systems,” Applied Mathematics and Computation, Vol. 219, No. 11, pp. 6224–6235, 2013.
10. F. Allgower, R. Findeisen, and C. Ebenbauer, “Nonlinear model predictive control,” Encyclopedia of Life Support Systems, Article Contribution 6.43.16.2, 2003.
11. E. G. Gilbert and I. Kolmanovsky, “Nonlinear tracking control in the presence of state and control constraints: a generalized reference governor,” Automatica, Vol. 38, No. 12, pp. 2063–2073, 2002.
12. T. Hu and Z. Lin, Control Systems with Actuator Saturation: Analysis and Design, Birkhauser, Boston, MA, 2001.
13. K. B. Ngo, R. Mahony, and Z. P. Jiang, “Integrator backstepping using barrier functions for systems with multiple state constraints,” Proc. 44th IEEE Conf. Decision and Control, pp. 8306–8312, 2005.
14. K. P. Tee, S. S. Ge, and E. H. Tay, “Barrier Lyapunov Functions for the control of output-constrained nonlinear systems,” Automatica, Vol. 45, No. 4, pp. 918–927, 2009.
15. S. I. Han and J. M. Lee, “Adaptive fuzzy backstepping dynamic surface control for output-constrained non-smooth nonlinear dynamic system,” International Journal of Control, Automation and Systems, Vol. 10, No. 4, pp. 684–696, 2012.
16. B. Ren, S. S. Ge, K. P. Tee, and T. H. Lee, “Adaptive neural control for output feedback nonlinear systems using a barrier Lyapunov function,” IEEE Trans. on Neural Networks, Vol. 21, No. 8, pp. 1339–1345, 2010.
17. K. P. Tee and S. S. Ge, “Control of nonlinear systems with partial state constraints using a barrier Lyapunov function,” International Journal of Control, Vol. 84, No. 12, pp. 2008–2023, 2011.
18. K. P. Tee, B. Ren, and S. S. Ge, “Control of nonlinear systems with time-varying output constraints,” Automatica, Vol. 47, No. 11, pp. 2511–2516, 2011.
19. H. Jeffreys, Methods of Mathematical Physics, Cambridge University Press, England, 1988.
20. M. M. Polycarpou and M. J. Mears, “Stable adaptive tracking of uncertain systems using nonlinearly parameterized on-line approximators,” International Journal of Control, Vol. 70, No. 3, pp. 363–384, 1998.
21. ∞ controller design with recurrent neural network for linear synchronous motor drive,” IEEE Trans. Ind. Electron., Vol. 50, No. 3, pp. 456–470, 2003.
22. S. S. Ge, C. C. Hang, T. H. Lee, and T. Zhang, Stable Adaptive Neural Network Control, Kluwer, Norwell, MA, 2001.
23. H. K. Khalil, Nonlinear Systems, Prentice-Hall, Upper Saddle River, NJ, 1996.
24. P. A. Ioannou and P. V. Kokotovic, Adaptive Systems with Reduced Models, Springer-Verlag, NY, 1983.
25. S. J. Yoo, J. B. Park, and Y. H. Choi, “Indirect adaptive control of nonlinear dynamic systems using self-recurrent wavelet neural networks via adaptive learning rates,” Information Sciences, Vol. 177, No. 15, pp. 3074–3098, 2007.