0
TECHNICAL PAPERS

An Adaptive State Filtering Algorithm for Systems With Partially Known Dynamics

[+] Author and Article Information
Alexander G. Parlos

Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843e-mail: a-parlos@tamu.edu

Sunil K. Menon

Honeywell Technology Center, 3660 Technology Drive, MN65-2500, Minneapolis, MN 55418e-mail: sunil@htc.honeywell.com

Amir F. Atiya

Department of Electrical Engineering, California Institute of Technology, MS 136-93, Pasadena, CA 91125e-mail: amir@work.caltech.edu

J. Dyn. Sys., Meas., Control 124(3), 364-374 (Jul 23, 2002) (11 pages) doi:10.1115/1.1485747 History: Received December 01, 2000; Online July 23, 2002
Copyright © 2002 by ASME
Your Session has timed out. Please sign back in to continue.

References

Kalman,  R. E., and Bucy,  R. S., 1961, “New Results in Linear Filtering and Prediction Theory,” ASME J. Basic Eng., 83, pp. 95–107.
Gelb, A., 1974, Applied Optimal Estimation, MIT Press, Cambridge, MA.
Jonsson,  G., and Palsson,  O. P., 1994, “An Application of Extended Kalman Filtering to Heat Exchanger Models,” ASME J. Dyn. Syst., Meas., Control, 116, pp. 257–264.
Haykin, S., 1999, Neural Networks: A Comprehensive Foundation, 2nd Edition, Prentice-Hall, Piscataway, NJ.
Lo,  J. T-H., 1994, “Synthetic Approach to Optimal Filtering,” IEEE Trans. Neural Netw., 5(5) Sept., pp. 803–811.
Elanayar,  S., and Shin,  Y. C., 1994, “Radial Basis Function Neural Network for Approximation and Estimation of Nonlinear Stochastic Dynamic Systems,” IEEE Trans. Neural Netw., 5(4) July, pp. 594–603.
Annaswamy,  A. M., and Yu,  S. H., 1996, “θ-Adaptive Neural Networks: A New Approach to Parameter Estimation,” IEEE Trans. Neural Netw., 7(4), pp. 594–603.
Parisini, T., Alessandri, A., Maggiore, M., and Zoppoli, R., 1997, “On Convergence of Neural Approximate Nonlinear State Estimators,” Proceedings of the 1997 American Control Conference, Vol. 3, June, pp. 1819–1822.
Alessandri,  A., Parisini,  T., and Zoppoli,  R., 1997, “Neural Approximators for Nonlinear Finite-Memory State Estimation,” Int. J. Control, 67(2), pp. 275–301.
Haykin,  S., Yee,  P., and Derbez,  E., 1997, “Optimum Nonlinear Filtering,” IEEE Trans. Neural Netw., 45(11) Nov., pp. 2774–2786.
Zhu, R., Chai, T., and Shao, C., 1997, “Robust Nonlinear Adaptive Observer Design using Dynamic Recurrent Neural Networks,” Proceedings of the 1997 American Control Conference, Vol. 2, June, pp. 1096–1100.
Habtom, R., and Litz, L., 1997, “Estimation of Unmeasured Inputs using Recurrent Neural Networks and the Extended Kalman Filter,” International Conference on Neural Networks, Vol. 4, pp. 2067–2071.
Dong,  X., Qui,  L., and Wang,  Z., 1997, “Neural Networks-based Nonlinear Adaptive Filters and On-line Fault Detection,” Control and Decision, 12(1) Jan., pp. 78–87.
Lei, J., Guangdong, H., and Jiang, J. P., 1997, “The State Estimation of the CSTR System Based on a Recurrent Neural Network Trained by HGAs,” International Conference on Neural Networks, Vol. 2, pp. 779–782.
Schenker,  B., and Agarwal,  M., 1998, “Predictive Control of a Bench-Scale Chemical Reactor Based on Neural-Network Models,” IEEE Trans. Control Syst. Technol., 6(3) May, pp. 388–400.
Stubberud, S. C., and Owen, M., 1998, “Targeted On-line Modeling for an Extended Kalman Filter Using Artificial Neural Networks,” Proceedings of the 1998 American Control Conference, Vol. 3, June, pp. 1852–1856.
Stubberud,  S. C., Owen,  M., and Lobbia,  R. N., 1998, “Adaptive Extended Kalman Filter Using Artificial Neural Networks,” International Journal of Smart Engineering System Design, 1(3), pp. 207–221.
Durovic, Z., and Kovacevic, B., 1998, “Adaptive Filtering using Neural Networks Approach,” Proceedings of the Mediterranean Electrotechnical Conference, Vol. 1, May, pp. 499–503.
Menon, S. K., Parlos, A. G., and Atiya, A. F., 2000, “Nonlinear State Filtering for Fault Diagnosis and Prognosis in Complex Systems Using Recurrent Neural Networks,” 4th Symposium on Fault Detection, Supervision and Safety for Technical Processes, IFAC SAFEPROCESS 2000, June.
Parlos, A. G., Menon, S. K., and Atiya, A. F., 1999, “Adaptive State Estimation Using Dynamic Recurrent Neural Networks,” Proceedings of the International Joint Conference on Neural Networks, June.
Grewal, M. S., and Andrews, A. P., 1993, Kalman Filtering: Theory and Practice, Prentice-Hall, Upper Saddle River, NJ.
Ljung, L., 1999, System Identification: Theory for the User, 2nd Edition, Prentice-Hall, Upper Saddle River, NJ.
Narendra,  K. S., and Parthasarathy,  K., 1990, “Identification and Control of Dynamic System Using Neural Networks,” IEEE Trans. Neural Netw., 1, pp. 4–27.
Barron,  A. R., 1994, “Approximation and Estimation Bounds for Artificial Neural Networks,” Journal of Machine Learning, 14, pp. 115–133.
Parlos,  A. G., Rais,  O. T., and Atiya,  A. F., 2000, “Multi-Step-Ahead Prediction in Complex Systems Using Dynamic Recurrent Neural Networks,” Neural Networks, 13(4–5), pp. 765–786.
Parlos,  A. G., Chong,  K. T., and Atiya,  A., 1994, “Application of the Recurrent Multilayer Perceptron in Modeling Complex Process Dynamics,” IEEE Trans. Neural Netw., 5, pp. 255–266.
Williams,  R., and Zipser,  D., 1989, “A learning algorithm for continually running fully recurrent neural networks,” Neural Comput., 1, pp. 270–280.
Atiya,  A., and Parlos,  A., 2000, “New Results on Recurrent Network Training: Unifying the Algorithms and Accelerating Convergence,” IEEE Trans. Neural Netw., 11, pp. 697–709.
Geiger, G., 1984, “Fault Identification of a Motor-Pump System using Parameter Estimation and Pattern Classification,” IFAC 9th Triennial World Congress, Dec.
Isermann,  R., 1985, “Process Fault Diagnosis with Parameter Estimation Methods,” IFAC Digital Computer Applications to Process Control, Dec., pp. 51–60.
Franklin, G. F., Powell, J. D., and Workman, M., 1998, Digital Control of Dynamic Systems, 3rd Edition, Addison Wesley Longman, Menlo Park, CA.
Choi, J. I., 1987, Nonlinear Digital Computer Control for the Steam Generator System in a Pressurized Water Reactor Plant, PhD thesis, MIT, Department of Nuclear Engineering.
Menon,  S. K., and Parlos,  A. G., 1992, “Gain-Scheduled Nonlinear Control of U-Tube Steam Generator Water Level,” Nuclear Science and Engineering, III(3), pp. 294–308.

Figures

Grahic Jump Location
Block diagram of the neural network state filter
Grahic Jump Location
Motor-pump system armature resistance and flux linkage filter block diagram
Grahic Jump Location
Motor-pump system armature resistance and flux linkage filter response using the test data set as inputs (high noise environment)
Grahic Jump Location
Motor-pump system armature resistance and flux linkage filter response using the test data set as inputs (high noise environment)
Grahic Jump Location
Block diagram of the UTSG riser void fraction filter
Grahic Jump Location
UTSG process riser void fraction filter response using the ramp input of the test data set (high noise environment)
Grahic Jump Location
UTSG process riser void fraction filter response using the step input of the test data set (high noise environment)

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In