Neural model adaptation and predictive control of a chemical process rig

IEEE Transactions on Control Systems Technology

Volumn 14, Issue 5, 2006, Pages 828-840

  Yu, Ding Li ^a   Yu, Ding Wen ^b   Gomm, J Barry ^a  

^a LIVERPOOL JOHN MOORES UNIVERSITY   (United Kingdom)

^b NORTHEASTERN UNIVERSITY AT QINHUANGDAO   (China)

Author keywords

Adaptive neural networks; Localized forgetting; Nonlinear model predictive control; Process control

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; ALGORITHMS; MATHEMATICAL MODELS; NEURAL NETWORKS; PREDICTIVE CONTROL SYSTEMS; RADIAL BASIS FUNCTION NETWORKS;

ADAPTATION ALGORITHM; ADAPTIVE NEURAL NETWORKS; CHEMICAL PROCESS RIG; NONLINEAR MODEL PREDICTIVE CONTROL;

PROCESS CONTROL;

EID: 33747679800     PISSN: 10636536     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCST.2006.876906     Document Type: Article

References (28)

1. S. J. Qin and T. A. Badgwell, J. C. Kantor, C. E. Garcia, and B. Carnahan, Eds., "An overview of industrial model predictive control technology," in Proc. 5th Int. Conf. Chem. Process Contr., 1997, pp. 232-256.
2. D. W. Clarke, C. Mohtadi, P. S. Tuffs, and P. S., "Generalized predictive control - Part I: The basic algorithm," Automatica, vol. 23, no. 2, pp. 149-160, 1987.
3. C. Cutler and B. Ramaker, "Dynamic matrix control: A computer control algorithm," presented at the Proc. 86th AlChE Nat. Meeting, Hoston, TX, Apr. 1979.
4. D. M. Prett and R. D. Gillette, "Optimization and constrained multivariable control of a catalytic cracking unit," presented at the Joint Autom. Contr. Conf., San Francisco, CA, 1980.
5. S. J. Qin and T. A. Badgwell, "An overview of nonlinear model predictive control applications," in Nonlinear Model Predictive Control. Cambridge, MA: Birkhauser, 2000, pp. 369-392.
6. G. D. Nicolao, L. Magni, and R. Scattolini, "Nonlinear receding horizon control of internal combustion engines," in Nonlinear Model Predictive Control. Cambridge, MA: Birkhauser, 2000, pp. 449-460.
7. S. Chen and S. A. Billings, "Representations of non-linear systems: The NARMAX model," Int. J. Control, vol. 49, pp. 1013-1032, 1988.
8. M. Wellers and H. Rake, "Nonlinear model predictive control based on stable Wiener and Hammerstein models," Progress Syst. Contr. Theory, vol. 26, pp. 357-366, 2000.
9. M. Schley, V. Prasad, L. P. Russo, and B. W. Bequette, "Nonlinear model predictive control of a styrene polymerization reactor," in Nonlinear Model Predictive Control. Cambridge, MA: Birkhauser, 2000, pp. 403-418.
10. T. L. Liao and C. M. Chiu, "Adaptive output tracking of unknown MIMO nonlinear systems using radial basis function neural networks," JSME Int. J. Series C - Mechan. Syst. Machine Elements Manuf., vol. 40, no. 1, pp. 52-59, 1997.
11. J. O. Jang, "Implementation of indirect neural-control for a non-linear two-robot MIMO system," Contr. Eng. Practice, vol. 9, no. 1, pp. 89-95, 2001.
12. S. Rohani, M. Haeri, and H. C. Wood, "Modeling and control of a continuous crystallization process - Part 2: Model predictive control," Comput. Chem. Eng., vol. 23, no. 3, pp. 279-286, 1999.
13. S. Piche, B. Sayyar-Rodsari, D. Johnson, and M. Gerules, "Nonlinear model predictive control," IEEE Contr. Syst. Mag., vol. 20, no. 3, pp. 53-62, Jun. 2000.
14. B. Schenker and M. Agarwal, "Predictive control of a bench-scale chemical reactor based on neural network models," IEEE Trans. Contr. Syst. Technol., vol. 6, no. 3, pp. 388-400, May 1998.
15. D. L. Yu and J. B. Gomm, "Implementation of neural network predictive control to a multivariable chemical reactor," Contr. Eng. Practice, vol. 11, pp. 1315-1323, 2003.
16. L. Yingwei, N. Sundararajan, and P. Saratchandran, "Identification of time-varying nonlinear systems using minimal radial basis function neural networks," Proc. Inst. Elect. Eng. D, vol. 144, no. 2, pp. 202-208, 1997.
17. N. B. Karayiannis and G. W. Mi, "Growing radial basis neural networks: Merging supervised and unsupervised learning with network growth techniques," IEEE Trans. Neural Netw., vol. 8, no. 6, pp. 1492-1506, Nov. 1997.
18. W. Luo and S. A. Billings, "Structure selective updating for nonlinear models and radial basis function networks," Int. J. Adap. Contr. Signal Process., vol. 12, pp. 325-345, 1998.
19. G. P. Liu, V. Kadirkamanathan, and S. A. Billings, "Variable neural networks for adaptive control of nonlinear systems," IEEE Trans. Syst. Man, Cybern. C, Appl. Rev., vol. 29, no. 1, pp. 34-43, Feb. 1999.
20. Y. Y. Yang and D. A. Linkens, "Adaptive neural network based approach for the control of continuously stirred tank reactor," Proc. Inst. Elect. Eng. D, vol. 141, no. 5, pp. 341-349, 1994.
21. C. Pereira, J. Henriques, and A. Dourado, "Adaptive RBFNN versus conventional self-tuning: Comparison of two parametric model approaches for non-linear control," Contr. Eng. Prac., vol. 8, no. 1, pp. 3-12, 2000.
22. K. S. Narendra and K. Parthasarathy, "Identification and control of dynamical systems using neural networks," IEEE Trans. Neural Netw., vol. 1, no. 1, pp. 4-27, Mar. 1990.
23. J. A. Leonard and M. A. Kramer, "Radial basis functions for classifying process faults," IEEE Control Syst. Mag., vol. 11, no. 3, pp. 31-38, Apr. 1991.
24. D. L. Yu, J. B. Gomm, and D. Williams, "A recursive orthogonal least squares algorithm for training RBF networks," Neural Process. Lett., vol. 5, no. 3, pp. 167-176, 1997.
25. J. E. Bobrow and W. Murray, "An algorithm for RLS identification of parameters that vary quickly with time," IEEE Trans. Autom. Control, vol. 38, no. 2, pp. 35-254, Feb. 1993.
26. J. B. Gomm and D. L. Yu, "Selecting redial basis function neural network centers with recursive orthogonal least squares training," IEEE Trans. Neural Netw., vol. 11, no. 2, pp. 306-314, Mar. 2000.
27. D. W. Yu, Development of first-principle models for the chemical reactor rig using optimization method Contr. Syst. Res. Group, Sch. Eng., Liverpool John Moores Univ., Liverpool, U.K., 2002, Tech. Rep..
28. D. L. Yu, J. B. Gomm, and D. Williams, "Neural model input selection for a MIMO chemical process," Eng. Appl. Artif. Intell., vol. 13, no. 1, pp. 15-23, 2000.