A data-based state feedback control method for a class of nonlinear systems

IEEE Transactions on Industrial Informatics

Volumn 9, Issue 4, 2013, Pages 2284-2292

  Wang, Zhuo ^a   Liu, Derong ^a  

^a University of Illinois at Chicago   (United States)

Author keywords

Control switch; Data based state feedback control; Fast sampling technique; Nonlinear systems; Real time control; Sampled state data; Zero order hold

Indexed keywords

CONTROL METHODS; FAST SAMPLING; FEEDBACK GAIN MATRIX; PRIOR KNOWLEDGE; SAMPLED STATE DATA; SYSTEM DYNAMICS; SYSTEM INFORMATION; ZERO-ORDER HOLDS;

MATHEMATICAL MODELS; NONLINEAR SYSTEMS; REAL TIME CONTROL; STATE FEEDBACK;

FEEDBACK CONTROL;

EID: 84886694214     PISSN: 15513203     EISSN: None     Source Type: Journal    
DOI: 10.1109/TII.2012.2227266     Document Type: Article

References (25)

1. T. Chai, Z. Hou, F. L. Lewis, A.Hussain, and D. Zhao, "Guest editorial: Special section on data-based control, modeling, and optimization," IEEE Trans. Neural Netw., vol. 22, no. 12, pp. 2150-2153, Dec. 2011.
2. D. Wang, "Robust data-driven modeling approach for real-time final product quality prediction in batch process operation," IEEE Trans. Ind. Inf., vol. 7, no. 2, pp. 371-377, May 2011.
3. G. Li, B. Liu, S. J. Qin, and D. Zhou, "Quality relevant data-driven modeling and monitoring of multivariate dynamic processes: The dynamic T-PLS approach," IEEE Trans. Neural Netw., vol. 22, no. 12, pp. 2262-2271, Dec. 2011.
4. D. Wang, J. Liu, and R. Srinivasan, "Data-driven soft sensor approach for quality prediction in a refining process," IEEE Trans. Ind. Inf., vol. 6, no. 1, pp. 11-17, Feb. 2010.
5. C. Caione, D. Brunelli, and L. Benini, "Distributed compressive sampling for lifetime optimization in dense wireless sensor networks," IEEE Trans. Ind. Inf., vol. 8, no. 1, pp. 30-40, Feb. 2012.
6. Z. Hou and S. Jin, "A novel data-driven control approach for a class of discrete-time nonlinear systems," IEEE Trans. Contr. Syst. Technol., vol. 19, no. 6, pp. 1549-1558, Nov. 2011.
7. A. Gomperts, A. Ukil, and F. Zurfluh, "Development and implementation of parameterized FPGA-based general purpose neural networks for online applications," IEEE Trans. Ind. Inf., vol. 7, no. 1, pp. 78-89, Feb. 2011.
8. B. Subudhi and D. Jena, "Nonlinear system identification using memetic differential evolution trained neural networks," Neurocomput., vol. 74, no. 10, pp. 1696-1709, May 2011.
9. C. Y. Yeh, W. R. Jeng, and S. J. Lee, "Data-based system modeling using a type-2 fuzzy neural network with a hybrid learning algorithm," IEEE Trans. Neural Netw., vol. 22, no. 12, pp. 2296-2309, Dec. 2011.
10. N. Dong and Z. Chen, "A novel data based control method based upon neural network and simultaneous perturbation stochastic approximation," Nonlinear Dynamics, vol. 67, no. 2, pp. 957-963, Jan. 2012.
11. I. Steinwart and A. Christmann, Support Vector Machines. New York: Springer, 2008.
12. S. Ertekin, L. Bottou, and C. L. Giles, "Nonconvex online support vector machines," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 2, pp. 368-381, Feb. 2011.
13. C. Gao, L. Jian, and S. Luo, "Modeling of the thermal state change of blast furnace hearth with support vector machines," IEEE Trans. Ind. Electron., vol. 59, no. 2, pp. 1134-1145, Feb. 2012.
14. C. T. Freeman, Z. Cai, E. Rogers, and P. L. Lewin, "Iterative learning control for multiple point-to-point tracking application," IEEE Trans. Contr. Syst. Technol., vol. 19, no. 3, pp. 590-600, May 2011.
15. S. Taniguchi, H. Kino, R. Ozawa, R. Ishibashi, M. Uemura, K. Kanaoka, and S. Kawamura, "Inverse dynamics of human passive motion based on iterative learning control," IEEE Trans. Syst., Man Cybern., Part A: Syst. Humans, vol. 42, no. 2, pp. 307-315, Mar. 2012.
16. X. Ruan, Z. Z. Bien, and Q. Wang, "Convergence characteristics of proportional-type iterative learning control in the sense of Lebesgue-p norm," IET Contr. Theory Appl., vol. 6, no. 5, pp. 707-714, Mar. 2012.
17. T. Chai, Y. Zhang, H. Wang, C. Y. Su, and J. Sun, "Data-based virtual unmodeled dynamics driven multivariable nonlinear adaptive switching control," IEEE Trans. Neural Netw., vol. 22, no. 12, pp. 2154-2172, Dec. 2011.
18. Z. S. Hou and S. T. Jin, "Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems," IEEE Trans. Neural Netw., vol. 22, pp. 2173-2188, Dec. 2011.
19. Y. Fujisaki and K. Saitou, "Virtual reference feedback tuning for controllers," in Proc. SICE Ann. Conf. 2011, Tokyo, Japan, Sep. 2011, pp. 1768-1769.
20. L. Campestrini, D. Eckhard, M. Gevers, and A. S. Bazanella, "Virtual reference feedback tuning for non-minimum phase plants," Automatica, vol. 47, no. 8, pp. 1778-1784, Aug. 2011.
21. D. Liberzon, Calculus of Variations and Optimal Control Theory: A Concise Introduction. Princeton, NJ: Princeton Univ. Press, 2012.
22. J. E. Gentle, Matrix Algebra: Theory, Computations, and Applications in Statistics. New York: Springer, 2007.
23. D. H. Zhou, X. He, Z. Wang, G. P. Liu, and Y. D. Ji, "Leakage fault diagnosis for an internet-based three-tank system: An experimental study," IEEE Trans. Contr. Syst. Technol., vol. 20, no. 4, pp. 857-870, Jul. 2012.
24. D. Nešić and A. R. Teel, "A framework for stabilization of nonlinear sampled-data systems based on their approximate discrete-time models," IEEE Trans. Autom. Contr., vol. 49, no. 7, pp. 1103-1122, Jul. 2004.
25. K. Ogata, Modern Control Engineering, 5th ed. Englewood Cliffs, NJ: Prentice Hall, 2009.