An adaptive recurrent neural-network controller using a stabilization matrix and predictive inputs to solve a tracking problem under disturbances

Neural Networks

Volumn 49, Issue , 2014, Pages 74-86

  Fairbank, Michael ^a   Li, Shuhui ^b   Fu, Xingang ^b   Alonso, Eduardo ^a   Wunsch, Donald ^c  

^a CITY UNIVERSITY   (United Kingdom)

^b University of Alabama   (United States)

^c University of Missouri Rolla   (United States)

Author keywords

Exploding gradients; Recurrent neural networks; Stabilization matrix; Tracking problem; Vector control

Indexed keywords

ADAPTIVE CRITIC DESIGNS; EXPLODING GRADIENTS; GENERATOR APPLICATIONS; GRID-CONNECTED CONVERTERS; INSTANTANEOUS RESPONSE; STABILIZATION MATRIX; TRACKING PROBLEM; VECTOR CONTROLS;

ADAPTIVE CONTROL SYSTEMS; RECURRENT NEURAL NETWORKS; STABILIZATION;

PROBLEM SOLVING;

ARCHITECTURE; ARTICLE; ARTIFICIAL NEURAL NETWORK; CASE STUDY; CONTROL SYSTEM; ELECTRIC POWER PLANT; INFORMATION PROCESSING; LEARNING ALGORITHM; OSCILLATION; PATTERN GENERATOR; PATTERN RECOGNITION; PHYSICAL PARAMETERS; PREDICTIVE VALUE; PRIORITY JOURNAL; RECURRENT NEURAL NETWORK; RENEWABLE ENERGY; RESPONSE TIME; SIMULATION; STABILIZATION MATRIX; TRACKING PROBLEM;

EXPLODING GRADIENTS; RECURRENT NEURAL NETWORKS; STABILIZATION MATRIX; TRACKING PROBLEM; VECTOR CONTROL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; COMPUTER SYSTEMS; NEURAL NETWORKS (COMPUTER); ONLINE SYSTEMS; PROBLEM SOLVING; SUPPORT VECTOR MACHINES;

EID: 84886388184     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2013.09.010     Document Type: Article

References (38)

1. Barnard E. Temporal-difference methods and Markov models. IEEE Transactions on Systems, Man and Cybernetics 1993, 23(2):357-365.
2. Bishop C.M. Neural networks for pattern recognition 1995, Oxford University Press.
3. Carrasco J.M., Franquelo L.G., Bialasiewicz J.T., Galván E., Guisado R.C.P., Prats M.A.M., et al. Power-electronic systems for the grid integration of renewable energy sources: a survey. IEEE Transactions on Industrial Electronics 2006, 53(4):1002-1016.
4. Dannehl J., Wessels C., Fuchs F.W. Limitations of voltage-oriented PI current control of grid-connected PWM rectifiers with LCL filters. IEEE Transactions on Industrial Electronics 2009, 56(2):380-388.
5. Fairbank M., Alonso E. The divergence of reinforcement learning algorithms with value-iteration and function approximation. Proceedings of the IEEE international joint conference on neural networks 2012 2012, 3070-3077. IEEE Press.
6. Feldkamp L.A., Puskorius G.V. A signal processing framework based on dynamic neural networks with application to problems in adaptation, filtering, and classification. Proceedings of the IEEE 1998, 86(11):2259-2277.
7. Figueres E., Garcerá G., Sandia J., Gonzalez-Espin F., Rubio J.C. Sensitivity study of the dynamics of three-phase photovoltaic inverters with an LCL grid filter. IEEE Transactions on Industrial Electronics 2009, 56(3):706-717.
8. Franklin G.F., Workman M.L., Powell D. Digital control of dynamic systems 1998, Addison-Wesley Longman Publishing Co., Inc. 3rd ed.
9. Golub G.H., Van Loan C.F. Matrix computations 1983, Johns Hopkins University Press, Baltimore, Maryland.
10. Hochreiter S., Schmidhuber J. Long short-term memory. Neural Computing 1997, 9(8):1735-1780.
11. Kirk D.E. Optimal control theory: an introduction 2004, Dover Publications.
12. Li S., Fairbank M., Wunsch D., Alonso E. Vector control of a grid-connected rectifier inverter using an artificial neural network. Proceedings of the IEEE international joint conference on neural networks 2012 2012, 1783-1789. IEEE Press.
13. Li S., Haskew T.A., Hong Y.K., Xu L. Direct-current vector control of three-phase grid-connected rectifier-inverter. Electric Power Systems Research 2011, 81(2):357-366.
14. Li S., Haskew T.A., Xu L. Control of HVDC light system using conventional and direct current vector control approaches. IEEE Transactions on Power Electronics 2010, 25(12):3106-3118.
15. Luo A., Tang C., Shuai Z., Tang J., Xu X.Y., Chen D. Fuzzy-PI-based direct-output-voltage control strategy for the STATCOM used in utility distribution systems. IEEE Transactions on Industrial Electronics 2009, 56(7):2401-2411.
16. Mohan N., Undeland T.M., Robbins W.P. Power electronics: converters, applications, and design 2002, John Wiley & Sons. 3rd ed.
17. Mullane A., Lightbody G., Yacamini R. Wind-turbine fault ride-through enhancement. IEEE Transactions on Power Systems 2005, 20(4):1929-1937.
18. Park J., Harley R., Venayagamoorthy G. New external neuro-controller for series capacitive reactance compensator in a power network. IEEE Transactions on Power Systems 2004, 19(3):1462-1472.
19. Pena R., Clare J.C., Asher G.M. Doubly fed induction generator using back-to-back PWM converters and its application to variable-speed wind-energy generation. IEE proceedings electric power applications, Vol. 143 1996, 231-241. IET.
20. Prokhorov D.V., Feldkamp L.A., Tyukin I.Y. Adaptive behavior with fixed weights in RNNs: an overview. Proceedings of the 2002 international joint conference on neural networks, 2002, Vol. 3 2002, 2018-2022. IEEE.
21. Prokhorov D., Wunsch D. Adaptive critic designs. IEEE Transactions on Neural Networks 1997, 8(5):997-1007.
22. Qiao W., Harley R., Venayagamoorthy G. Fault-tolerant optimal neurocontrol for a static synchronous series compensator connected to a power network. IEEE Transactions on Industry Applications 2008, 44(1):74-84.
23. Qiao W., Harley R.G., Venayagamoorthy G.K. Coordinated reactive power control of a large wind farm and a STATCOM using heuristic dynamic programming. IEEE Transactions on Energy Conversion 2009, 24(2):493-503.
24. Qiao W., Venayagamoorthy G.K., Harley R.G. Optimal wide-area monitoring and nonlinear adaptive coordinating neurocontrol of a power system with wind power integration and multiple FACTS devices. Neural Networks 2008, 21(2):466-475.
25. Qiao W., Venayagamoorthy G.K., Harley R.G. Real-time implementation of a STATCOM on a wind farm equipped with doubly fed induction generators. IEEE Transactions on Industry Applications 2009, 45(1):98-107.
26. Rabelo B.C., Hofmann W., da Silva J.L., de Oliveira R.G., Silva S.R. Reactive power control design in doubly fed induction generators for wind turbines. IEEE Transactions on Industrial Electronics 2009, 56(10):4154-4162.
27. Rall L.B. Automatic differentiation: techniques and applications. Lecture notes in computer science 1981, vol. 120.
28. Riedmiller, M., & Braun, H. (1993). A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In Proc. of the IEEE intl. conf. on neural networks. San Francisco, CA (pp. 586-591).
29. Venayagamoorthy G.K., Harley R.G., Wunsch D.C. Comparison of heuristic dynamic programming and dual heuristic programming adaptive critics for neurocontrol of a turbogenerator. IEEE Transactions on Neural Networks 2002, 13(3):764-773.
30. Venayagamoorthy G., Harley R., Wunsch D. Implementation of adaptive critic-based neurocontrollers for turbogenerators in a multimachine power system. IEEE Transactions on Neural Networks 2003, 14(5):1047-1064.
31. Wang C., Nehrir M.H. Short-time overloading capability and distributed generation applications of solid oxide fuel cells. IEEE Transactions on Energy Conversion 2007, 22(4):898-906.
32. Wang F.-Y., Zhang H., Liu D. Adaptive dynamic programming: an introduction. IEEE Computational Intelligence Magazine 2009, 4(2):39-47.
33. Werbos P.J. Backpropagation through time: what it does and how to do it. Proceedings of the IEEE 1990, 78(10):1550-1560.
34. Werbos P.J. Approximating dynamic programming for real-time control and neural modeling. Handbook of intelligent control 1992, 493-525. Van Nostrand Reinhold, New York, (Chapter 13). D.A. White, D.A. Sofge (Eds.).
35. Werbos, P.J. (1998). Stable adaptive control using new critic designs. Sections 7.7-7.8. eprint arxiv:adap-org/9810001.
36. Werbos P.J. Backwards differentiation in AD and neural nets: past links and new opportunities. Lecture notes in computational science and engineering 2005, 15-34. Springer. H.M. Bücker, G. Corliss, P. Hovland, U. Naumann, B. Norris (Eds.).
37. Werbos P.J., McAvoy T., Su T. Neural networks, system identification, and control in the chemical process industries. Handbook of intelligent control 1992, 339-343. Van Nostrand Reinhold, New York, (Chapter 10) Sections 10.6.1-10.6.2, Version on author's homepage with errata. URL www.werbos.com. D.A. White, D.A. Sofge (Eds.).
38. Xu L., Wang Y. Dynamic modeling and control of DFIG-based wind turbines under unbalanced network conditions. IEEE Transactions on Power Systems 2007, 22(1):314-323.