A support vector machine based control application to the experimental three-tank system

ISA Transactions

Volumn 49, Issue 3, 2010, Pages 376-386

  Iplikci, Serdar ^a  

^a PAMUKKALE UNIVERSITY   (Turkey)

Author keywords

Generalized predictive control; MIMO control; MIMO modeling; Support vector machines

Indexed keywords

LEVELING (MACHINERY); MIMO SYSTEMS; PREDICTIVE CONTROL SYSTEMS; TANKS (CONTAINERS);

COMPACT FORMULATIONS; CONTROL APPLICATIONS; GENERALIZED PREDICTIVE CONTROL; GRADIENT INFORMATIONS; LIQUID LEVEL CONTROL; MIMO CONTROLS; MIMO MODELS; REFERENCE TRAJECTORIES;

SUPPORT VECTOR MACHINES;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; FORECASTING; INDUSTRY; NONLINEAR SYSTEM; STATISTICS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; FORECASTING; INDUSTRY; NONLINEAR DYNAMICS;

EID: 77955662607     PISSN: 00190578     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isatra.2010.03.013     Document Type: Article

References (44)

1. D.W. Clarke, and C. Mohtadi Properties of generalized predictive control Automatica 25 6 1989 859 875
2. E.F. Camacho Constrained generalized predictive control IEEE Transactions on Automatic Control 38 2 1993 327 332
3. J. Richalet Industrial applications of model-based predictive control Automatica 29 1993 1251 1274
4. D.W. Clarke Application of generalized predictive control to industrial processes IEEE Control Systems Magazine 122 1988 49 55
5. S.J. Qin, and T.A. Badgwell A survey of industrial model predictive control technology Control Engineering Practice 11 2003 733 764
6. J.A. Richalet, A. Rault, J.L. Testud, and J. Papon Model predictive heuristic control: applications to an industrial process Automatica 14 1978 413 428
7. Cutler CR, Ramaker BL. Dynamic matrix control: a computer control algorithm. In: Proceedings of the joint automatic control conference. 1980.
8. De Keyser RMC, Van Cauwenberghe AR. Extended prediction self-adaptive control. In: Proceedings of the 7th IFAC symposium on identification and system parameter estimation. 1985.
9. R. Soeterboek Predictive control: a unified approach 1992 Prentice-Hall Englewood Cliffs (NY)
10. D.W. Clarke, C. Mohtadi, and P.C. Tuffs Generalized predictive controlpart 1: the basic algorithm Automatica 23 1987 137 148
11. D.W. Clarke, C. Mohtadi, and P.C. Tuffs Generalized predictive controlpart 2: extensions and interpretations Automatica 23 1987 149 163
12. D.W. Clarke Advances in model-based predictive control 1994 Oxford University Press Oxford
13. Soloway D, Haley PJ. Neural generalized predictive control: a NewtonRaphson algorithm. In: Proceedings of the IEEE international symposium on intelligent control. 1996.
14. M.T. Hagan, H.B. Demuth, and O. De Jesus An introduction to the use of neural networks in control systems International Journal of Robust and Nonlinear Control 12 2002 959 985
15. J.M. Zamarreno, and P. Vega Neural predictive control: application to a highly non-linear system Engineering Applications of Artificial Intelligence 12 1999 149 158
16. P.H. Sørensen, M. Nørgaard, O. Ravn, and N.K. Poulsen Implementation of neural network based nonlinear predictive control Neurocomputing 28 1999 37 51
17. M. Fischer, O. Nelles, and R. Isermann Adaptive predictive control of a heat exchanger based on a fuzzy model Control Engineering Practice 6 2 1998 259 269 (Pubitemid 28271362)
18. J. Espinosa, and J. Vandewalle Predictive control using fuzzy models Advances in soft computing engineering design and manufacturing 1999 Springer London
19. J.Q. Hu, and E. Rose Generalized predictive control using a neuro-fuzzy model International Journal of Systems Science 30 1999 117 122
20. H. Sarimveis, and G. Bafas Fuzzy model predictive control of non-linear processes using genetic algorithms Fuzzy Sets and Systems 139 2003 59 80
21. D.L. Yu, and J.B. Gomm Implementation of neural network predictive control to a multivariable chemical reactor Control Engineering Practice 11 2003 1315 1323
22. J. Chen, and Y. Yea Neural network-based predictive control for multivariable processes Chemical Engineering Communications 189 2002 865 894
23. M. Lawrynczuk Efficient nonlinear predictive control based on structured neural models International Journal of Applied Mathematics and Computer Science 19 2 2009 233 246
24. M. Lawrynczuk A family of model predictive control algorithms with artificial neural networks International Journal of Applied Mathematics and Computer Science 17 2 2007 217 232
25. J.A. Roubos, S. Mollov, R. Bbuska, and H.B. Verbruggen Fuzzy model-based predictive control using TakagiSugeno models International Journal of Approximate Reasoning 22 1999 3 30
26. N. Li, S.Y. Li, and Y.G. Xi Multi-model predictive control based on the TakagiSugeno fuzzy models: a case study Information Sciences 165 2004 247 263
27. V.N. Vapnik The nature of statistical learning theory 1995 Springer-Verlag London
28. V.N. Vapnik Statistical learning theory 1998 Wiley-Interscience New York
29. V.N. Vapnik The support vector method of function estimation Nonlinear modeling advanced black box techniques 1998 Kluwer Academic Publishers Boston
30. Smola AJ, Schlkopf B. A tutorial on support vector regression. NeuroCOLT Tech. rep. NC-TR-98-030. Royal Holloway College, University of London; 1998.
31. N. Cristianini, and J.S. Taylor An introduction to support vector machines and other kernel-based learning methods 2000 Cambridge University Press New York
32. B. Schlkopf, C.J.C. Burges, and A.J. Smola Advances in kernel methods: support vector learning 1999 The MIT Press Cambridge (MA, USA)
33. S. Iplikci Support vector machines-based generalized predictive control International Journal of Robust and Nonlinear Control 16 17 2006 843 862
34. S. Iplikci Online trained support vector machines-based generalized predictive control of non-linear systems International Journal of Adaptive Control and Signal Processing 20 10 2006 599 621 (Pubitemid 46065916)
35. Liu H, Wu H, Qian F. Double inverted pendulum control based on support vector machines and fuzzy inference. In: Proceedings of the international symposium on neural networks. 2006.
36. X.C. Xi, A.N. Poo, and S.K. Chou Support vector regression model predictive control on a HVAC plant Control Engineering Practice 15 8 2007 897 908
37. H. Wang, D. Pi, and Y. Sun Online SVM regression algorithm-based adaptive inverse control Neurocomputing 70 46 2007 952 959
38. J. Nocedal, and S.J. Wright Numerical optimization 1999 Springer New York
39. P. Venkataraman Applied optimization with MATLAB programming 2002 Wiley-Interscience New York
40. DTS200laboratory setup three-tank system. Amira GmbH, Duisburg; 2000.
41. K.J. Astrom, and T. Hagglund PID controllers: theory, design and tuning 1995 ISA Research Triangle Park NC
42. A. O'Dwyer Handbook of PI and PID controller tuning rules 2003 Imperial College Press London
43. W.D. Chang A multi-crossover genetic approach to multivariable PID controllers tuning Expert Systems with Applications 33 3 2007 620 626
44. O.K. Erol, and I. Eksin A new optimization method: Big Bang Big Crunch Advances in Engineering Software 37 2006 106 111