Real-time recurrent neural state estimation

IEEE Transactions on Neural Networks

Volumn 22, Issue 3, 2011, Pages 497-505

  Alanis, Alma Y ^a   Sanchez, Edgar N ^b   Loukianov, Alexander G ^b   Perez, Marco A ^a  

^a UNIVERSITY OF GUADALAJARA   (Mexico)

^b UNIDAD GUADALAJARA   (Mexico)

Author keywords

Discrete time nonlinear systems; extended Kalman filtering; neural state estimation; real time implementation; recurrent neural networks

Indexed keywords

EXTENDED KALMAN FILTERS; INDUCTION MOTORS; NONLINEAR CONTROL SYSTEMS; REAL TIME CONTROL; STATE ESTIMATION;

DISCRETE-TIME NONLINEAR SYSTEMS; EXTENDED KALMAN FILTERING; NONLINEAR DISCRETE-TIME; PARAMETER UNCERTAINTY; REAL-TIME IMPLEMENTATIONS; RECURRENT HIGH-ORDER NEURAL NETWORKS; THREE PHASE INDUCTION MOTOR; UNKNOWN NONLINEAR SYSTEMS;

RECURRENT NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; NONLINEAR SYSTEM; STANDARD; TEACHING; TIME;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; TEACHING; TIME FACTORS;

EID: 79952188860     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2010.2103322     Document Type: Article

References (42)

1. D. F. Coutinho and L. P. F. A. Pereira, "A robust Luenberger-like observer for induction machines," in Proc. 31st Annu. Conf. IEEE Ind. Electron. Soc., Nov. 2005, pp. 1-5.
2. G. Besançon, Nonlinear Observers and Applications (Lecture Notes in Control and Information Sciences), vol. 363. Berlin, Germany: Springer- Verlag, 2007.
3. J. A. Farrell and M. M. Polycarpou, Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches. New York: Wiley, 2006.
4. A. S. Poznyak, E. N. Sanchez, and W. Yu, Differential Neural Networks for Robust Nonlinear Control. Singapore: World Scientific, 2001.
5. S. Nicosia and A. Tornambè, "High-gain observers in the state and parameter estimation of robots having elastic joints," Syst. Control Lett., vol. 13, no. 4, pp. 331-337, Nov. 1989. (Pubitemid 20622349)
6. R. Grover and P. Y. C. Hwang, Introduction to Random Signals and Applied Kalman Filtering, 2nd ed. New York: Wiley, 1992.
7. A. J. Krener and A. Isidori, "Linearization by output injection and nonlinear observers," Syst. Control Lett., vol. 3, no. 1, pp. 47-52, Jun. 1983.
8. J. Li and Y. Zhong, "Comparison of three Kalman filters for speed estimation of induction machines," in Proc. 40th IAS Annu. Meeting Ind. Appl. Conf., vol. 3. Oct. 2005, pp. 1792-1797. (Pubitemid 44042692)
9. Y. Liu, Z. Wang, and X. Liu, "Design of exponential state estimators for neural networks with mixed time delays," Phys. Lett. A, vol. 364, no. 5, pp. 401-412, May 2007.
10. Z. Wang, D. W. C. Ho, and X. Liu, "State estimation for delayed neural networks," IEEE Trans. Neural Netw., vol. 16, no. 1, pp. 279-284, Jan. 2005. (Pubitemid 40241927)
11. F. Chen and M. W. Dunnigan, "Comparative study of a sliding-mode observer and Kalman filters for full state estimation in an induction machine," IEE Proc. Electric Power Appl., vol. 149, no. 1, pp. 53-64, Jan. 2002. (Pubitemid 34138413)
12. H. Huang, G. Feng, and J. Cao, "Robust state estimation for uncertain neural networks with time-varying delay," IEEE Trans. Neural Netw., vol. 19, no. 8, pp. 1329-1339, Aug. 2008.
13. B. Walcott and S. Zak, "State observation of nonlinear uncertain dynamical systems," IEEE Trans. Autom. Control, vol. 32, no. 2, pp. 166-170, Feb. 1987. (Pubitemid 17534802)
14. Y. H. Kim and F. L. Lewis, High-Level Feedback Control with Neural Networks. Singapore: World Scientific, 1998.
15. A. U. Levin and K. S. Narendra, "Control of nonlinear dynamical systems using neural networks. II. Observability, identification, and control," IEEE Trans. Neural Netw., vol. 7, no. 1, pp. 30-42, Jan. 1996. (Pubitemid 126811726)
16. R. Marino, "Adaptive observers for single output nonlinear systems," IEEE Trans. Autom. Control, vol. 35, no. 9, pp. 1054-1058, Sep. 1990.
17. E. N. Sanchez and L. J. Ricalde, "Trajectory tracking via adaptive recurrent control with input saturation," in Proc. Int. Joint Conf. Neural Netw., vol. 1. Portland, OR, Jul. 2003, pp. 359-364.
18. J. Liang, Z. Wang, and X. Liu, "State estimation for coupled uncertain stochastic networks with missing measurements and time-varying delays: The discrete-time case," IEEE Trans. Neural Netw., vol. 20, no. 5, pp. 781-793, May 2009.
19. A. Y. Alanis, E. N. Sanchez, and A. G. Loukianov, "Discrete-time adaptive backstepping nonlinear control via high-order neural networks," IEEE Trans. Neural Netw., vol. 18, no. 4, pp. 1185-1195, Jul. 2007. (Pubitemid 47098890)
20. M. Elbuluk, T. Liu, and I. Husain, "Neural network-based model reference adaptive systems for high performance motor drives and motion controls," IEEE Trans. Ind. Appl., vol. 38, no. 3, pp. 879-886, May-Jun. 2002. (Pubitemid 34750465)
21. S. Haykin, Kalman Filtering and Neural Networks. New York: Wiley, 2001.
22. S. Singhal and L. Wu, "Training multilayer perceptrons with the extended Kalman algorithm," in Advances in Neural Information Processing Systems, vol. 1, D. S. Touretzky, Ed. San Mateo, CA: Morgan Kaufmann, 1989, pp. 133-140.
23. S. Singhal and L. Wu, "Training multilayer perceptrons with the extended Kalman algorithm," in Advances in Neural Information Processing Systems, vol. 1, D. S. Touretzky, Ed. San Mateo, CA: Morgan Kaufmann, 1989, pp. 133-140.
24. R. J. Williams and D. Zipser, "A learning algorithm for continually running fully recurrent neural networks," Neural Comput., vol. 1, no. 2, pp. 270-280, 1989.
25. A. Y. Alanis, E. N. Sanchez, A. G. Loukianov, and M. A. Perez-Cisneros, "Real-time discrete neural block control using sliding modes for electric induction motors," IEEE Trans. Control Syst. Technol., vol. 18, no. 1, pp. 11-21, Jan. 2010.
26. Z. Andonov, "Overview of induction motor parameter identification techniques," in Proc. 11th Int. Symp. Power Electron., Novi Sad, Yugoslavia, Nov. 2001, pp. 241-245.
27. V. Utkin, J. Guldner, and J. Shi, Sliding Mode Control in Electromechanical Systems. New York: Taylor & Francis, 1999.
28. F. Van der Heijden, D. de Ridder, Classification, Parameter Estimation, and State Estimation: An Engineering Approach Using MATLAB. New York: Wiley, 2004.
29. S. S. Ge, J. Zhang, and T. H. Lee, "Adaptive neural network control for a class of MIMO nonlinear systems with disturbances in discrete-time," IEEE Trans. Syst., Man Cybern., Part B: Cybern., vol. 34, no. 4, pp. 1630-1645, Aug. 2004.
30. H. K. Khalil, Nonlinear Systems, 2nd ed. Upper Saddle River, NJ: Prentice-Hall, 1996.
31. G. A. Rovithakis and M. A. Chistodoulou, Adaptive Control with Recurrent High-Order Neural Networks. Berlin, Germany: Springer- Verlag, 2000.
32. A. G. Loukianov, J. Rivera, and J. M. Cañedo, "Discrete-time sliding mode control of an induction motor," in Proc. IFAC, Barcelona, Spain, Jul. 2002, pp. 106-111.
33. J. Jiang and J. Holtz, "High dynamic speed sensorless AC drive with on-line model parameter tuning for steady-state accuracy," IEEE Trans. Ind. Electron., vol. 44, no. 2, pp. 240-246, Apr. 1997. (Pubitemid 127828646)
34. W. Leonard, Control of Electrical Drives, 2nd ed. New York: Springer- Verlag, 2001.
35. F. Khorrami, P. Krishnamurthy, and H. Melkote, Modeling and Adaptive Nonlinear Control of Electric Motors. Berlin, Germany: Springer-Verlag, 2003.
36. H. K. Khalil, E. G. Strangas, and S. Jurkovic, "Speed observer and reduced nonlinear model for sensorless control of induction motors," IEEE Trans. Control Syst. Technol., vol. 17, no. 2, pp. 327-339, Mar. 2009.
37. J. Quiñones, "Real time implementation of a three phase induction motor control," M.S. dissertation, Cinvestav, Unidad Guadalajara, Guadalajara, Jalisco, Mexico, 2006.
38. L. Ljung, System Identification: Theory for the User, 2nd ed. Upper Saddle River, NJ: Prentice-Hall, 1999.
39. M. Norgaard, O. Ravn, N. K. Poulsen, and L. K. Hansen, Neural Networks for Modelling and Control of Dynamic Systems. New York: Springer-Verlag, 2000.
40. T. Söderström and P. Stoica, System Identification. Englewood Cliffs, NJ: Prentice-Hall, 1989.
41. L. A. Feldkamp, D. V. Prokhorov, and T. M. Feldkamp, "Simple and conditioned adaptive behavior from Kalman filter trained recurrent networks," Neural Netw., vol. 16, nos. 5-6, pp. 683-689, Jun. 2003. (Pubitemid 36794342)
42. A. Y. Alanis, E. N. Sanchez, and A. G. Loukianov, "Real-time output tracking for induction motors by recurrent high-order neural network control," in Proc. IEEE 17th Mediterranean Conf. Control Autom., Thessaloniki, Greece, Jun. 2009, pp. 868-873.