Adaptive neural output regulation control of a solar power plant

Control Engineering Practice

Volumn 18, Issue 10, 2010, Pages 1183-1196

  Henriques, J ^a   Gil, P ^a,b   Cardoso, A ^a   Carvalho, P ^a   Dourado, A ^a  

^a UNIVERSITY OF COIMBRA   (Portugal)

^b UNIVERSIDADE NOVA DE LISBOA   (Portugal)

Author keywords

Discrete time nonlinear adaptive control; On line learning; Output regulation; Recurrent neural networks; Solar power plants

Indexed keywords

COMBINATORIAL OPTIMIZATION; E-LEARNING; EIGENVALUES AND EIGENFUNCTIONS; RECURRENT NEURAL NETWORKS; SOLAR ENERGY; SOLAR POWER PLANTS;

ADAPTIVE NONLINEAR CONTROL; EIGENVALUE ASSIGNMENT PROBLEM; NON-LINEAR ADAPTIVE CONTROLS; ONLINE LEARNING; OUTPUT REGULATION; OUTPUT REGULATION CONTROL; OUTPUT REGULATION THEORIES; REGULATION PROBLEMS;

ADAPTIVE CONTROL SYSTEMS;

EID: 78651521126     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conengprac.2010.06.001     Document Type: Article

References (55)

1. Arahal M., Berenguela M., Camacho E. Neural identification applied to predictive control of a solar plant. Control Engineering Practice 1998, 6-10:333-334.
2. Bambang R. Nonlinear active noise control using EKF-based recurrent fuzzy neural networks. International Journal of Hybrid Intelligent Systems archive 2007, 4:4.
3. Becerikli Y., Konar A., Samad T. Intelligent optimal control with dynamic neural networks 2003, Vol. 16. Elsevier Science Ltd, Neural Networks archive, p. 2.
4. Berenguel, M., Camacho, E., Rubio, F. (1993). Simulation software package for the Acurex. Internal Report, Sevilla.
5. Cai X. Advanced architecture and training algorithms for recurrent neural networks 2006, University of Missouri/Rolla Piblisher.
6. Camacho E., Berenguel M., Rubio F. Advanced control of solar plants 1997, Springer, London.
7. Camacho E., Rubio F., Berenguel M., Valenzuela L. A survey on control schemes for distributed solar collector fields. Part I: Modeling and basic control approaches. Solar Energy 2007, 81:1240-1251.
8. Camacho E., Rubio F., Berenguel M., Valenzuela L. A survey on control schemes for distributed solar collector fields. Part II: Advanced control approaches. Solar Energy 2007, 81:1252-1272.
9. Camacho E., Rubio F., Hughes F. Self-tuning control of a solar power plant with a distributed colletor field. IEEE Control Systems Magazine 1992, 12(No. 2):72-78.
10. Cardoso, A., Henriques, J., & Dourado, A. (1999). Fuzzy supervisor and feedforward control of a solar power plant using accessible disturbances, In Proceedings of the ECC99, fifth European control conference, Karlsruhe, Germany.
11. Carr J. Applications of center manifold theory 1981, Springer Verlag, New York, USA.
12. Carrillo, M., Keyser, R., & Ionescu, C. (2007a). Comparison between model based controllers for a distributed collectors field. In Proceedings of the European control conference 2007, Kos, Greece, Paper No. 519.
13. Carrillo, M., Keyser, R., & Ionescu, C. (2007b). Application of a Smith predictor Based Nonlinear predictive controller to a solar power plant. In Proceedings of the seventh IFAC symposium on nonlinear control systems, NOLCOS 2007, Pretoria, South Africa.
14. Castillo-Toledo B. Output regulation of nonlinear systems with more inputs than outputs. International Journal of Control 1994, 57(6):1343-1356.
15. Castillo-Toledo, B., & DiGennaro, S. (1991). Asymptotic output tracking for SISO nonlinear discrete systems. In Proceedings of the 30th conference on decision and control, Brighton, England, pp. 1802-1806.
16. Castillo-Toledo B., DiGennaro S., Normand-Cyrot D. Nonlinear regulation for a class of discrete-time systems. Systems Control Letters 1993, 20:57-65.
17. Coito F., Lemos J., Silva R., Mosca E. Adaptive control of a solar energy plant: Exploiting accessible disturbances. International Journal of Adaptive Control and Signal Processing 1997, 11(4):327-342.
18. Flores A., Saez D., Araya J., Berenguel M., Cipriano A Fuzzy predictive control of a solar power plant. IEEE Transactions on Fuzzy Systems 2005, 13.
19. Francis B., Wonham W. The internal model principle for linear multivariable systems. Journal of Applied Mathematical Optimistion 1975, 2:170-194.
20. Gil, P., Henriques, J., Carvalho, P., Duarte-Ramos, H., & Dourado, A. (2002). Adaptive neural model-based predictive control of a solar power plant, In Proccedings of the international joint conference on neural networks, Honolulu, Hawai, May 12-17.
21. Gil, P., Henriques, J., Duarte-Ramos, H., & Dourado, A. (2001). Recurrent neural networks and feedback linearization for a solar power plant control. In Proceedings of the EUNIT01, Tenerife, 13-16 December, 2001.
22. Gupta, M., Jin, L., & Homma, N. (2003). Static and Dynamic neural networks: From fundamentals to advanced theory.
23. Han M., Shi Z., Wang W. Modeling dynamic system by recurrent neural network with state variables, Lecture Notes in Computer Science 2004, Springer, Berlin.
24. Haykin S. Neural networks and learning machines 2008, Prentice Hall. 3rd Ed.
25. Henriques J., Cardoso A., Dourado A. Supervision and C-means clustering of PID controllers for a solar power plant. International Journal of Approximate Reasoning 1999, 22:73-91.
26. Henriques, J., Gil, P., & Dourado, A. (2002). Scheduling of PID controllers by means of a neural network. In Proceedings of the international joint conference on neural networks, Honolulu, Hawai, May 12-17.
27. Hopfield J. Neural networks and physical systems with emergent collective computational abilities. National Academy of Sciences USA 1982, 79:2554-2558.
28. Huang J., Rugh W. An approximation method for the nonlinear servomechanism problem. IEEE Transactions on Automatic Control 1992, 37(9):1395-1398.
29. Huang J., Rugh W. Stabilization on zero-error manifolds and the nonlinear servomechanism problem. IEEE Transactions on Automatic Control 1999, 37(7):1003-1009.
30. Isidori A., Byrnes C. Output regulation for nonlinear systems. IEEE Transactions on Automatic Control 1990, 26:131-140.
31. Khalil, H. (1992). Non-linear systems. New York, USA: Macmillan Publishing Company.
32. Kharaajoo M. Predictive control of a solar power plant with neuro-fuzzy identification and evolutionary programming optimization, Book Series Lecture Notes in Computer Science 2004, Springer, Berlin.
33. Lalot, S. (2006). Artificial Neural networks in solar thermal energy systems. In S. Kalogirou (Ed.), Artificial intelligence in energy and renewable energy systems.
34. Luenberger D. An introduction to observers. IEEE Transactions on Automatic Control 1971, 16:596-602.
35. López, J. (1996). Acurex field DAS, Internal Report 08/96, Plataforma Solar de Almería.
36. Mazenc F. Differentiable Lyapunov function and center manifold theory, Book Series Lecture Notes in Control and Information Sciences 2001, Vol. 259. Springer, Berlin.
37. Monaco, A., & Normand-Cyrot, D. (1987). Minimum phase nonlinear discrete-time systems and feedback stabilization. In: Proceedings of the 23th conference on decision and control, Los Angeles, pp. 979-986.
38. Narendra K., Parthasarathy K. Gradient methods for the optimisation of dynamical systems containing neural networks. IEEE Transactions on Neural Networks 1991, 2(2):252-262.
39. Norgaard M., Ravan O., Poulsen L., Hansen L. Neural networks for modelling and control of dynamic systems 2000, Springer-Verlag, London.
40. Pattamavorakun S., Pattamavorakun S. A comparison of selected training algorithms for recurrent neural networks, Book Series Lecture Notes in Computer Science 2006, Springer, Berlin.
41. Pickhardt, R., & Silva, R. (1998). Application of a nonlinear predictive controller to a solar power plant. In: Proceedings of the IEEE conference on control applications, Trieste.
42. Principe J., Euliano N., Lefebvre W. Neural and adaptive systems: fundamentals through simulation 2000, John Wiley & Sons.
43. Rubio, F., Hughes, M., & Camacho, E. (1989). Self-tuning PI control of a solar power plant. In Proceedings of the IFAC symposium on adaptive systems in control and signal processing, pp. 335-340.
44. Rubio F., Camacho E., Bergenguel M. Control de Campos de Colectores Solares. Revista Iberoamericana de Automática e Informática Industrial 2006, 3(4):26-45.
45. Saberi, A., & Stoorvogel, A. (2000). On output regulation for linear systems. Technical Report, Department of Electrical & Computer Engineering, Rutgers University, NJ, USA.
46. Stephanopoulos, G., & Han, C. (1994). Intelligent systems in process engineering. In Proceedings of the fifth international symposium on process system enginnering, Kyongju, Korea.
47. Sun, F., Zhang, J., Tan, Y., Cao, J., & Yu, W. (2008). Advances in neural networks. In Proceedings of the ISNN 2008: Fifth international symposium on neural networks, Beijing, China, September 24-28.
48. Suratgar, A., Tavakoli, M., & Hoseinabadi, A. (2005). Modified Levenberg-Marquardt method for neural networks training, In Proceedings of World Academy of Science, Engineering and Technology, Vol. 6.
49. Sureshbabu N., Rugh W. Output regulation with derivative information. IEEE Transactions on Automatic Control 1995, 40:1755-1766.
50. Sureshbabu N., Rugh W. On output regulation for discrete-time nonlinear systems. Automatica 1997, 33(9):1683-1689.
51. Thau F. Observing the state of nonlinear dynamical systems. International Journal of Control 1973, 17:471-479.
52. Weiyao L., Huang J. Neural-network-based approximate output regulation of discrete-time nonlinear systems. IEEE Transactions on Neural Networks 2007, vol 18:4.
53. Werbos P. Backpropagation through time: What it does and how to do it. Proceedings of the IEEE 1990, 78(10):1550-1560.
54. Williams R., Zipser D. Gradient-based learning algorithms for recurrent networks and their computational complexity. Backpropagation: Theory, architectures and applications 1995, 433-486. Y. Chauvin, D. Rumelhart (Eds.).
55. Zhang Y., Wang J. Recurrent neural network for nonlinear output regulation. Automatica 2001, 37:1161-1173.