Online trained support vector machines-based generalized predictive control of non-linear systems

International Journal of Adaptive Control and Signal Processing

Volumn 20, Issue 10, 2006, Pages 599-621

  Iplikci, S ^a  

^a PAMUKKALE UNIVERSITY   (Turkey)

Author keywords

Adaptive control; Generalized predictive control; Online control; Support vector machines

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; NONLINEAR SYSTEMS; QUALITY CONTROL;

GENERALIZED PREDICTIVE CONTROL; ONLINE CONTROL; SUPPORT VECTOR MACHINES;

PREDICTIVE CONTROL SYSTEMS;

EID: 33846087392     PISSN: 08906327     EISSN: 10991115     Source Type: Journal    
DOI: 10.1002/acs.919     Document Type: Article

References (37)

1. Clarke DW, Mohtadi C, Tuffs PC. Generalized predictive control - part 1: the basic algorithm. Automatica 1987; 23:137-148.
2. Clarke DW, Mohtadi C, Tuffs PC. Generalized predictive control - part 2: the basic algorithm. Automatica 1987: 23:149-163.
3. Clarke DW. Advances in model-based predictive control. Advances in Model-Based Predictive Control. Oxford University Press: Oxford, 1994.
4. Qin SJ, Badgwell TA. A survey of industrial model predictive control technology. Control Engineering Practice 2003; 11:733-764.
5. Clarke DW, Mohtadi C. Properties of generalized predictive control. Automation 1989; 25(6):859-875.
6. Camacho EF. Constrained generalized predictive control. IEEE Transactions on Automatic Control 1993; 38(2): 327-332.
7. Soloway D, Haley PJ. Neural generalized predictive control: a Newton-Raphson algorithm. Proceedings of the IEEE International Symposium on Intelligent Control, Dearborn, MI, 1996; 277-282.
8. Hagan MT, Demuth HB, De Jesus O. An introduction to the use of neural networks in control systems. International Journal of Robust and Nonlinear Control 2002; 12:959-985.
9. Zamarrefio JM, Vega P. Neural predictive control: application to a highly nonlinear system. Engineering Applications of Artificial Intelligence 1999; 12:149-158.
10. Sørensen PH, Nergaard M, Ravn O, Poulsen NK, Implementation of neural network based nonlinear predictive control. Neurocomputing 1999; 28:37-51.
11. Huang YL, Lou HH, Gong JP, Edgar TF. Fuzzy model predictive control. IEEE Transactions on Fuzzy Systems 2000; 8:665-678.
12. Li N, Li SY, Xi YG. Multi-model predictive control based on the Takagi-Sugeno fuzzy models: a case study. Information Sciences 2004; 165:247-263.
13. Espinosa J, Vandewalle J. Predictive control using fuzzy models. Advances in Soft Computing Engineering Design and Manufacturing. Springer: London, 1999.
14. Martinez M, Senent JS, Blasco X. Generalized predictive control using genetic algorithms (GAGPC). Engineering Applications of Artificial Intelligence 1998; 11:355-367.
15. Matko D, Kavsek-Biasizzo K, Kocijan J. Neuro-fuzzy model-based control. Journal of Intelligent and Robotic Systems 1998; 23:249-265.
16. Zhang J, Morris AJ. Recurrent neuro-fuzzy networks for nonlinear process modeling. IEEE Transactions on Neural Networks 1999; 10(2):313- 326.
17. Sarimveis H, Bafas G. Fuzzy model predictive control of nonlinear processes using genetic algorithms. Fuzzy Sets and Systems 2003; 139:59-80.
18. Iplikci S. Support vector machines based generalized predictive control. International Journal of Robust and Nonlinear Control, accepted.
19. Hu JQ, Rose E. Generalized predictive control using a neuro-fuzzy model. International Journal of Systems Science 1999; 30(1):117-122.
20. Tan K.K., Lee TH, Huang SN, Leu FM. Adaptive-predictive control of a class of SISO nonlinear systems. Dynamics and Control 2001; 11:151-174.
21. Mahfouf M, Abbod MF, Linkens DA. Online elicitation of Mamdani-type fuzzy rules via TSK-based generalized predictive control. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 2003; 333:465-475.
22. Wang DH, Soh CB. Adaptive neural model-based decentralized predictive control. International Journal of Systems Science 2000;31(1):119- 130.
23. Parlos AG, Parthasarathy S, Atiya AF. Neuro-predictive process control using online controller adaptation. IEEE Transactions on Control Systems Technology 2001; 9(5):741-754.
24. Alexandridis A, Sarimveis H. Nonlinear adaptive model predictive control based on self-correcting neural network models. AIChE Journal 2005; 51(9):2495-2506.
25. Ma J, Theiler J, Perkins S. Accurate online support vector regression. Neural Computation 2003; 15:2683-2703.
26. Nocedal J, Wright SJ. Numerical Optimization. Springer Series in Operations Research, Springer: New York, 1999.
27. Venkataraman P. Applied Optimization with MATLAB Programming. Wiley: New York, 2002.
28. Vapnik V. The Nature of Statistical Learning Theory. Springer: London, 1995.
29. Vapnik V. Statistical Learning Theory. Wiley: New York, 1998.
30. Vapnik V. The Support Vector Method of Function Estimation. Nonlinear Modeling Advanced Block Box Techniques. Kluwer Academic Publishers: Boston, 1998.
31. Cristianini N, Taylor JS. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press: New York, 2000.
32. Schölkopf B, Burges CJC, Smola AJ. Advances in Kernel Methods: Support Vector Learning. The MIT Press: Cambridge, MA, 1999.
33. Smola AJ, Schölkopf B. A tutorial on support vector regression. NeuroCOLT Technical Report No. NC-TR-98-030. Royal Holloway College, University of London, London, 1998.
34. Cherkassky V, Ma Y. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 2004; 17:113-126.
35. Šindelař R, Babulka R. Input selection for nonlinear regression models. IEEE Transactions on Fuzzy Systems 2004; 12:688-696.
36. Efe MO, Abadoglu E, Kaynak O. A novel analysis and design of a neural network assisted nonlinear controller for a bioreactor. International Journal of Robust and Nonlinear Control 1999; 9:799-815.
37. Wu W, Chou YS. Adaptive feedforward and feedback control of nonlinear time-varying uncertain systems. International Journal of Control 1999; 72(12):1127-1138.