Stable adaptive control with recurrent neural networks for square MIMO non-linear systems

Engineering Applications of Artificial Intelligence

Volumn 22, Issue 4-5, 2009, Pages 702-717

  Zerkaoui, Salem ^a   Druaux, Fabrice ^a   Leclercq, Edouard ^a   Lefebvre, Dimitri ^a,b  

^a UNIVERSITÉ DU HAVRE   (France)

^b IUT   (France)

Author keywords

Adaptive control; Fully connected recurrent neural networks; Lyapunov function; Multivariable systems; Stability

Indexed keywords

ADAPTIVE CONTROL; CLOSED-LOOP PERFORMANCE; CONTROL PROBLEMS; CONTROL SCHEMES; FULLY CONNECTED RECURRENT NEURAL NETWORKS; INDIRECT ADAPTIVE CONTROL; LYAPUNOV APPROACH; MODEL UNCERTAINTIES; MULTI VARIABLES; NEURAL CONTROLLER; NEURAL MODELS; NON-LINEAR; ON-LINE PARAMETER; RATE PARAMETERS; REAL-TIME RECURRENT LEARNING; REAL-WORLD; SENSOR NOISE; SUFFICIENT CONDITIONS; TENNESSEE EASTMAN CHALLENGE;

ADAPTIVE CONTROL SYSTEMS; ASYMPTOTIC STABILITY; DIFFERENTIAL EQUATIONS; EDUCATION; LINEAR SYSTEMS; LYAPUNOV FUNCTIONS; MULTIVARIABLE SYSTEMS; NONLINEAR SYSTEMS; SYSTEM STABILITY; UNCERTAINTY ANALYSIS;

RECURRENT NEURAL NETWORKS;

EID: 67349113480     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2008.12.005     Document Type: Article

References (32)

1. Atiya A.F., and Parlos A.G. New results on recurrent network training: unifying the algorithms and accelerating convergence. IEEE Transactions on Neural Networks 11 3 (2000) 697-709
2. Chang W.F., and Mak M.W. A conjugate gradient learning algorithm for recurrent neural networks. Neurocomputing 24 1-3 (1999) 173-189
3. Chow T.W.S., and Fang Y. A recurrent neural-network-based real-time learning control strategy applying to nonlinear systems with unknown dynamics. IEEE Transactions on Industrial Electronics 45 1 (1998) 151-161
4. Downs J.J., and Vogel E.F. A plant-wide industrial control problem. Computers and Chemical Engineering 17 (1993) 245-255
5. Druaux, F., Leclercq, E., Lefebvre, D., 2004. Adaptive neural network control for uncertain or unknown non-linear systems. In: Proceedings of IEEE-MMAR ′04, 30 auguste-2 septembre, Miedzyzdroje, Poland, pp. 1309-1314.
6. Funahashi K. On the approximate realization of continuous mappings by neural networks. Neural Networks 2 (1989) 183-192
7. Ge S.S., Hang C.C., and Zhang T. Stable adaptive control for multivariable nonlinear systems with a triangular control structure. IEEE Transactions on Automatic Control 45 6 (2000) 1221-1225
8. Ge, S.S., Wang, C., Tan, Y.H., 2001. Adaptive control of partially known nonlinear multivariable systems using neural networks. In: Proceedings of the 2001 IEEE International Symposium on Intelligent Control. September 5-7, Mexico City, Mexico.
9. Ge S.S., and Wang C. Direct adaptive NN control of a class of nonlinear systems. IEEE Transactions on Neural Networks 13 1 (2002) 214-221
10. Hammer B. On the approximation capability of recurrent neural networks. Neurocomputing 31 (2000) 107-124
11. Juricek, B.C., Seborg, D.E., Larimore, W.E., 2005, Process control applications of subspaces and regression-based identification and monitoring methods. In: Proceedings of the ACC, Portland, Oregon, USA.
12. Khalil H.K. Lyapunov stability. In: Levine W.S. (Ed). The Control Handbook (1996), CRC Press and IEEE Press, New York 889-895
13. Leclercq E., Druaux F., Lefebvre D., and Zerkaoui S. Autonomous learning algorithm for fully connected recurrent networks. Neurocomputing 63 (2005) 25-44
14. Narendra K.S., and Parthasarathy K. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks 1 1 (1990) 4-27
15. Renders, J.M., Saerens, M., Bersini, H., 1994. Adaptive neurocontrol of MIMO systems based on stability theory, IEEE-ICNN, Orlando, pp. 2476-2481.
16. Sanner R.M., and Slotine J.-J.E. Gaussian networks for direct adaptive control. IEEE Transactions on Neural Networks 3 (1992) 837-863
17. Satyanarayan, S.R., Balakrishna, K., 1995. A composite neural architecture and algorithm for nonlinear system identification, Artificial Networks in Engineering, St Louis, MO, USA.
18. Scorletti G., Fromion V., Font S., 2003. Automatique fréquentielle : des critères graphiques à l'optimisation LMI. RS-JESA-37. Analyse et commande des systèmes, pp. 161-185.
19. Singhal, A., 2000. Tennessee Eastman plant simulation with base control system of McAvoy and Ye., Research report, Department of Chemical Engineering, University of California, Santa Barbara, USA.
20. Tian L., and Collins C. A dynamic recurrent neural network-based controller for a rigid-flexible manipulator system. Mechatronics 14 (2004) 471-490
21. Wai R.J., and Lin F.J. Adaptive recurrent neural network control for linear induction motor. IEEE Transactions on Aerospace and Electronic Systems 37 4 (2001) 1176-1192
22. Wang D., and Bao P. Enhancing the estimation of plant Jacobian for adaptive neural inverse control. Neurocomputing 34 (2000) 99-115
23. Wang J. Sensitivity identification enhanced control strategy for nonlinear process systems. Computers and Chemical Engineering 27 (2003) 1631-1640
24. Williams R.J., and Zipser D. A learning algorithm for continually running fully recurrent neural networks. Neural Computation 1 2 (1989) 270-280
25. Williams R.J., and Zipser D. Gradient-based learning algorithms for recurrent networks and their computational complexity. In: Chauvin Y., and Rumelhart D.E. (Eds). Back-propagation: Theory, Architectures and Applications (1995), Lawrence Erlbaum Publishers, Hillsdale, NJ 433-486
26. Yan, N., Ricker, N.L., 1995. Multi-objective control of the Tennessee Eastman Challenge Process. In: Proceedings of the ACC, Seattle, Washington, USA.
27. Yan, N., Ricker, N.L., 1997. On-line optimisation of the Tennessee Eastaman Challenge Process. In: Proceedings of the ACC, Albuquerque, New Mexico, USA.
28. Yeh T.-M., Huang M.-C., and Huang C.-T. Estimate of process compositions and plantwide control from multiple secondary measurements using artificial neural networks. Computers and Chemical Engineering 27 (2003) 55-72
29. Zerkaoui, S., Druaux, F., Leclercq, E., Lefebvre, D., 2005, Commande adaptative par réseau de neurones hyper-connectés: étude de la stabilité et de la robustesse, JDMACS05, Lyon, France.
30. Zerkaoui, S., Druaux, F., Leclercq, E., Lefebvre, D., 2006. Robust stability analysis for indirect neural adaptive control. In: Proceedings of the ICC, Glasgow, Scotland, UK.
31. Zhihong M., Wu H.R., and Palaniswami M. An adaptive tracking controller using neural networks for a class of nonlinear systems. IEEE Transactions on Neural Networks 9 5 (1998) 947-955
32. Zipser D. A subgrouping strategy that reduces complexity and seeds up learning in recurrent networks. Neural Computation 1 (1989) 552-558