Adaptive decentralized PID controllers design using JITL modeling methodology

Journal of Process Control

Volumn 22, Issue 9, 2012, Pages 1531-1542

  Yang, Xin ^a   Jia, Li ^b   Chiu, Min Sen ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b SHANGHAI UNIVERSITY   (China)

Author keywords

Decentralized PID controller; Just in Time Learning; Lyapunov method; Multivariable systems

Indexed keywords

BENCHMARK DESIGNS; CONTROL PERFORMANCE; CONTROLLER PARAMETER; DECENTRALIZED PID CONTROLLER; JUST-IN-TIME LEARNING; MODELING METHODOLOGY; PID DESIGN; TRACKING ERRORS; UPDATING ALGORITHM;

ALGORITHMS; BENCHMARKING; ELECTRIC CONTROL EQUIPMENT; LYAPUNOV METHODS; MULTIVARIABLE SYSTEMS; THREE TERM CONTROL SYSTEMS;

DESIGN;

EID: 84866067015     PISSN: 09591524     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jprocont.2012.05.018     Document Type: Article

References (31)

1. C. Kravaris, and J.C. Kantor Geometric methods for nonlinear process control Industrial & Engineering Chemistry Research 29 1990 2295 2323
2. D.A. Lawrence, and W.J. Rugh Gain scheduling dynamic linear controllers for a nonlinear plant Automatica 31 1995 381 390
3. K.U. Klatt, and S. Engell Gain-scheduling trajectory control of a continuous stirred tank reactor Computers and Chemical Engineering 22 1998 491 502 (Pubitemid 28444242)
4. L.C. To, M.O.G. Tadée, and P. Le Page Implementation of a differential geometric nonlinear controller on an industrial evaporator system Control Engineering Practice 6 1998 1309 1319 (Pubitemid 128383961)
5. C. Riverol, and V. Napolitan Use of neural networks as a tuning method for an adaptive PID - application in a heat exchanger Chemical Engineering Research & Design 78 2000 1115 1119
6. W.D. Chang, R.C. Hwang, and J.G. Hsieh A self-tuning PID control for a class of nonlinear systems based on the Lyapunov approach Journal of Process Control 12 2002 233 242
7. K.K. Tan, S. Huang, and R. Ferdous Robust self-tuning PID controller for nonlinear systems Journal of Process Control 12 2002 753 761 (Pubitemid 34732189)
8. R.J. Brendel, B.A. Ogunnaike, and P.S. Dhurjati Nonlinear PI controllers based on low-order empirical process models Industrial & Engineering Chemistry Research 42 2003 4668 4677 (Pubitemid 37204125)
9. A. Andrasik, A. Meszaros, and S.E. de Azevedo On-line tuning of a neural PID controller based on plant hybrid modeling Computers & Chemical Engineering 28 2004 1499 1509 (Pubitemid 38609098)
10. B.H. Bisowarno, Y.C. Tian, and M.O. Tadé Adaptive control of an ETBE reactive distillation column Journal of Chemical Engineering of Japan 37 2004 210 216 (Pubitemid 38502956)
11. J. Chen, and T.C. Huang Applying neural networks to on-line updated PID controllers for nonlinear process control Journal of Process Control 14 2004 211 230
12. J.Y. Gao, and H.M. Budman Design of robust gain-scheduled PI controllers for nonlinear processes Journal of Process Control 15 2005 807 817 (Pubitemid 40952586)
13. H.P. Huang, J.C. Jeng, and K.Y. Luo Auto-tune system using single-run relay feedback test and model-based controller design Journal of Process Control 15 2005 713 727
14. M. Shahrokhi, and G.R. Baghmisheh Modeling, simulation and control of a methanol synthesis fixed-bed reactor Chemical Engineering Science 60 2005 4275 4286
15. T.H. Pan, S.Y. Li, and W.J. Cai Lazy learning-based online identification and adaptive PID control: a case study for CSTR process Industrial & Engineering Chemistry Research 46 2007 472 480
16. A. Altinten, F. Ketevanlioglu, S. Erdogan, H. Hapoglu, and M. Alpbaz Self-tuning PID control of jacketed batch polystyrene reactor using genetic algorithm Chemical Engineering Journal 138 2008 490 497 (Pubitemid 351380744)
17. O. Arrieta, A. Visioli, and R. Vilanova PID autotuning for weighted servo/regulation control operation Journal of Process Control 20 2010 472 480
18. D.W. Aha, D. Kibler, and M.K. Albert Instance-based learning algorithms Machine Learning 6 1991 37 66 (Pubitemid 21727227)
19. D.W. Aha Lazy learning Artificial Intelligence Review 11 1997 7 10
20. C.G. Atkeson, A.W. Moore, and S. Schaal Locally weighted learning Artificial Intelligence Review 11 1997 11 73 (Pubitemid 127508233)
21. G. Bontempi, H. Bersini, and M. Birattari The local paradigm for modeling and control: from neuro-fuzzy to lazy learning Fuzzy Sets and Systems 121 2001 59 72 (Pubitemid 32446651)
22. M.W. Braun, D.E. Rivera, and A. Stenman A 'model-on-demand' identification methodology for non-linear process systems International Journal of Control 74 2001 1708 1717
23. C. Cheng, and M.S. Chiu A new data-based methodology for nonlinear process modeling Chemical Engineering Science 59 2004 2801 2810 (Pubitemid 38763544)
24. K. Fujiwara, M. Kano, S. Hasebe, and A. Takinami Soft-sensor development using correlation-based just-in-time modeling AIChE Journal 55 2009 1754 1765
25. C. Cheng, and M.S. Chiu Adaptive single-neuron controller design for nonlinear process control Journal of Chemical Engineering of Japan 41 2008 785 795
26. I. Nuella, C. Cheng, and M.S. Chiu Adaptive PID controller design for nonlinear systems Industrial & Engineering Chemistry Research 48 2009 4877 4883
27. Y. Kansha, L. Jia, and M.S. Chiu Self-tuning PID controllers based on the Lyapunov approach Chemical Engineering Science 63 2008 2732 2740
28. A. Raich, and A. Cinar Statistical process monitoring and disturbance diagnosis in multivariable continuous processes AIChE Journal 42 1996 995 1009 (Pubitemid 126468139)
29. P. Daoutidis, M. Soroush, and C. Kravaris Feedforward feedback-control of multivariable nonlinear processes AIChE Journal 36 1990 1471 1484
30. A. Uppal, W.H. Ray, and A.B. Poore Dynamic behavior of continuous stirred tank reactors Chemical Engineering Science 29 1974 967 985
31. C.L. Chen, T.C. Wang, and S.H. Hsu An LMI approach to H-infinity PI controller design Journal of Chemical Engineering of Japan 35 2002 83 93