With the ever increasing complexity of industrial systems, model-based control has encountered difficulties and is facing problems, while the interest in data-based control has been booming. This paper gives an overview of data-based control, which divides it into two subfields, intelligent modeling and direct controller design. In the two subfields, some important methods concerning data-based control are intensively investigated. Within the framework of data-based modeling, main modeling technologies and control strategies are discussed, and then fundamental concepts and various algorithms are presented for the design of a data-based controller. Finally, some remaining challenges are suggested.
Corresponding Author(s):
SUN Changyin,Email:cysun@seu.edu.cn
引用本文:
. Data-based intelligent modeling and control for nonlinear systems[J]. Frontiers of Electrical and Electronic Engineering in China, 2011, 6(2): 291-299.
Chaoxu MU, Changyin SUN. Data-based intelligent modeling and control for nonlinear systems. Front Elect Electr Eng Chin, 2011, 6(2): 291-299.
Brundle M, Naedele M. Security for process control systems: an overview. IEEE Security & Privacy , 2008, 6(6): 24-29 doi: 10.1109/MSP.2008.150
2
Katayama T, Mckelvey T, Sano A, Cassandras C G, Campi M C. Trends in systems and signals: status report prepred by the IFAC coordinating committee on systems and signals. Annual Reviews in Control , 2006, 30(1): 5-17 doi: 10.1016/j.arcontrol.2006.01.001
3
Chai T Y. Challenges of optimal control for plant-wide production processes in terms of control and optimization theories. Acta Automatica Sinica , 2009, 35(6): 641-648 (in Chinese) doi: 10.3724/SP.J.1004.2009.00641
4
Lee J H, Lee K S. Iterative learning control applied to batch processes: an overview. Control Engineering Practice , 2007, 15(10): 1306-1318 doi: 10.1016/j.conengprac.2006.11.013
5
Hou Z S, Xu J X. On data-driven control theory: the state of the art and perspective. Acta Automatica Sinica , 2009, 35(6): 650-667 (in Chinese) doi: 10.3724/SP.J.1004.2009.00650
6
Hand D J, Mannila H, Smyth P. Principles of Data Mining. Cambridge: The MIT Press, 2000
7
Han J W, Kamber M. Data Mining: Concepts and Techniques. San Francisco: Morgan Kaufmann, 2006
8
Witten I H, Frank E. Data Mining: Practical Machine Learning Tools and Techniques. 2nd ed. San Francisco: Morgan Kaufmann, 2005
9
Sun C Y, Yu W. Neural network for control, robotics and diagnostics. Neural Computing & Applications , 2008, 17(4): 325-326 doi: 10.1007/s00521-008-0181-7
10
Hou Z G, Zeng Z G, Sun C Y. Computational intelligence for optimization, modeling and control. Neural Computing & Applications , 2009, 18(5): 407-408 doi: 10.1007/s00521-009-0265-z
11
Zhu D Q, Shi H. Artificial Neural Network Theory and Application. Beijing: Science Press, 2006 (in Chinese)
12
Deng N Y, Tian Y J. A New Method of Data Mining — Support Vector Machine. Beijing: Science Press, 2006 (in Chinese)
13
Sun C Y, Ju P, Li L F. Fuzzy modeling technique with PSO algorithm for short-term load forecasting. Lecture Notes in Computer Science , 2006, 4223: 933-936 doi: 10.1007/11881599_116
14
Sun C Y, Song J Y, Li L F, Ju P. Implementation of hybrid short-term load forecasting system with analysis of temperature sensitivities. Soft Computing , 2008, 12(7): 633-638 doi: 10.1007/s00500-007-0252-1
15
Wu Z L, Li C H, Zhu J, Huang J. A semi-supervised SVM for manifold learning. In: Proceedings of the 18th International Conference on Pattern Recognition . 2006, 2: 490-493
16
Adankon M M, Cheriet M, Biem A. Semisupervised least squares support vector machine. IEEE Transactions on Neural Networks , 2009, 20(12): 1858-1870 doi: 10.1109/TNN.2009.2031143
17
Xu L L, White M, Schuurmans D. Optimal reverse prediction, a unified perspective on supervised, unsupervised and semi-supervised learning. In: Proceedings of the 26th International Conference on Machine Learning . 2009, 1137-1144
18
Xu J X, Hou Z S. Notes on data-driven system approaches. Acta Automatica Sinica , 2009, 35(6): 668-675 doi: 10.3724/SP.J.1004.2009.00668
19
Crammer K, Dekel O, Keshet J, Shalev-Shwartz S, Singer Y. Online passive-aggressive algorithms. Journal of Machine Learning Research , 2006, 7(8): 551-585
20
Kivinen J, Smola A J, Williamson R C. Online learning with kernels. IEEE Transactions on Signal Processing , 2004, 52(8): 2165-2176 doi: 10.1109/TSP.2004.830991
21
Cheng L, Vishwanathan S V N, Schuurmans D, Wang S, Caelli T. Implicit online learning with kernels. Advances in Neural Information Processing Systems , 2007, 19: 249-256
22
Dai X Z,Wang WC, Ding Y H, Sun Z Y. “Assumed inherent sensor” inversion based ANN dynamic soft-sensing method and its application in erythromycin fermentation process. Computers and Chemical Engineering , 2006, 30(8): 1203-1225 doi: 10.1016/j.compchemeng.2006.02.001
23
Wang H, Pi D Y, Sun Y X. Online SVM regression algorithmbased adaptive inverse control. Neurocomputing , 2007, 70(4-6): 952-959
24
Garcia C E, Morari M. Internal model control-1: a unifying review and some new results. Industrial Engineering Chemistry Process Design and Development , 1982, 21(2): 308-323 doi: 10.1021/i200017a016
25
Garcia C E, Morari M. Internal model control-2: design procedure for multivariable systems. Industrial Engineering Chemistry Process Design and Development , 1985, 24(3): 472-484 doi: 10.1021/i200029a043
26
Zhou Y, Chen Q W, Hu W L. New developments of research on internal model control. Control Theory & Applications , 2004, 21(3): 475-482
27
Nahas E P. Nonlinear internal model control strategy for neural network models. Computers Chemical Engineering , 1992, 16(12): 1039-1057 doi: 10.1016/0098-1354(92)80022-2
28
Wang D C, Fang T J. Internal model control approach based on support vector machines. Control Theory & Applications , 2004, 21(1): 85-88
29
Stephanopoulos G, Huang H P. The 2-port control system. Chemical Engineering Science , 1986, 41(6): 1611-1630 doi: 10.1016/0009-2509(86)85241-1
30
Hu Q, Saha P, Rangaiah G P. Internal model control with feedback compensation for uncertain non-linear systems. International Journal of Control , 2001, 74(14): 1456-1466 doi: 10.1080/00207170110070545
31
Song F H, Zheng E H. Nonlinear internal-model control based on support vector machine. Control Theory & Applications , 2008, 25(6): 1067-1071
32
Cueli J R, Bordons C. Iterative nonlinear model predictive control. Stability, robustness and applications. Control Engineering Practice , 2008, 16(9): 1023-1034 doi: 10.1016/j.conengprac.2007.11.003
33
Tsaia P F, Chub J Z, Janga S S, Shiehc S S. Developing a robust model predictive control architecture through regional knowledge analysis of artificial neural networks. Journal of Process Control , 2002, 13(5): 423-435 doi: 10.1016/S0959-1524(02)00067-7
34
Huang G S, Dexter L A. Realization of robust nonlinear model predictive control by offline optimisation. Journal of Process Control , 2008, 18(5): 431-438 doi: 10.1016/j.jprocont.2007.11.001
35
Wang L P, Young P C. An improved structure for model predictive control using non-minimal state space realisation. Journal of Process Control , 2006, 16(4): 355-371 doi: 10.1016/j.jprocont.2005.06.016
36
Xi X C, Poo A N, Chou S K. Support vector regression model predictive control on a HVAC plant. Control Engineering Practice , 2007, 15(8): 897-908 doi: 10.1016/j.conengprac.2006.10.010
37
Al Seyab R K, Cao Y. Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation. Journal of Process Control , 2008, 18(6): 568-581 doi: 10.1016/j.jprocont.2007.10.012
38
Mu C X, Zhang R M, Sun C Y. LS-SVM predictive control based on PSO for nonlinear systems. Control Theory & Applications , 2010, 27(2): 164-168
39
Spall J C. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control , 1992, 37(3): 332-341 doi: 10.1109/9.119632
40
Spall J C, Chin D C. Traffic-responsive signal timing for system-wide traffic control. Transportation Research, Part C: Emerging Technologies , 1997, 5(3-4): 153-163 doi: 10.1016/S0968-090X(97)00012-0
41
Spall J C, Cristion J A. Model-free control of nonlinear stochastic systems with discrete-time measurements. IEEE Transactions on Automatic Control , 1998, 43(9): 1198-1210 doi: 10.1109/9.718605
42
Hou Z S. The parameter identication, adaptive control and model free learning adaptive control for nonlinear systems. Dissertation for the Doctoral Degree . Shenyang: Northeastern University, 1994 (in Chinese)
43
Hou Z S. Nonparametric Models and Its Adaptive Control Theory. Beijing: Science Press, 1999 (in Chinese)
44
Safonov M G, Tsao T C. The unfalsified control concept and learning. IEEE Transactions on Automatic Control , 1997, 42(6): 843-847 doi: 10.1109/9.587340
45
Yamamoto K, Takao G, Yamada T. Design of a data-driven PID control. IEEE Transactions on Control System Technology , 2009, 17(1): 29-39 doi: 10.1109/TCST.2008.921808
46
Hjalmarsson H, Gunnarsson S, Gevers M. A convergent iterative restricted complexity control design scheme. In: proceedings of the 33rd IEEE Conference on Decision and Control . 1994, 1735-1740
47
Hjalmarsson H. Iterative feedback tuning: an overview. International Journal of Adaptive Control and Signal Processing , 2002, 16(5): 373-395 doi: 10.1002/acs.714
48
Hjalmarsson H. Control of nonlinear systems using iterative feedback tuning. In: Proceedings of the American Control Conference . 1998, 2083-2087
49
Lee J M, Lee J H. Approximate dynamic programming-based approaches for input-output data-driven control of nonlinear processes. Automatica , 2005, 41(7): 1281-1288 doi: 10.1016/j.automatica.2005.02.006
50
Bellman R E. Dynamic Programming. Princeton: Princeton University Press, 1957
51
Si J, Wang Y. Online learning control by association and reinforcement. IEEE Transactions on Neural Networks , 2001, 12(2): 264-276 doi: 10.1109/72.914523
52
Abu-Khalaf M, Lewis F L. Nearly optimal control laws for nonlinear systems with saturating actuators using a nerual network HJB approach. Automatica , 2005, 41(5): 779-791 doi: 10.1016/j.automatica.2004.11.034
53
Zhang H, Wei Q, Luo Y. A novel infinite-time optimal tracking control scheme for a class of discrete-time nonlinear system based on greedy HDP iteration algorithm. IEEE Transactions on Systems, Man, and Cybernetics — Part B: Cybernetics , 2008, 38(4): 937-942 doi: 10.1109/TSMCB.2008.920269
54
Zhang H, Luo Y, Liu D R. Neural-network-based nearoptimal control for a class of discrete-time affine nonlinear systems with control constraint. IEEE Transactions on Neural Networks , 2009, 20(9): 1490-1503 doi: 10.1109/TNN.2009.2027233
55
Arimoto S, Kawamura S, Miyazaki F. Bettering operation of dynamic systems by learning: a new control theory for servomechanism or mechatronic system. In: Proceedings of the 23rd Conference on Decision and Control . 1984, 1064-1069 doi: 10.1109/CDC.1984.272176
56
Chen Y, Wen C, Sun M. A robust high-order P-type iterative learning controller using current-iteration tracking error. International Journal of Control , 1997, 68(2): 331-342 doi: 10.1080/002071797223640
57
Chen Y Q, Wen C Y. Iterative Learning Control: Convergence, Robustness and Applications. Lecture Notes in Control and Information Sciences . Berlin: Springer-Verlag, 1999
58
Amann N, Owens D H, Rogers E. 2D systems theory applied to learning control systems. In: Proceedings of the 33rd Conference on Decision and Control . 1994, 985-986
59
Luca A, Paesano G, Ulivi G. A frequency-domain approach to learning control: implementation for a robot manipulator. IEEE Transations on Industrial Electronics , 1992, 39(1): 1-10 doi: 10.1109/41.121905
60
Deng H, Xu Z, Li H X. A novel neural internal model control for multi-input multi-output nonlinear discrete-time processes. Journal of Process Control , 2009, 19(8): 1392-1400 doi: 10.1016/j.jprocont.2009.04.011
