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From Feedback Control to Complexity Management: A Personal Perspective

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Switching and Learning in Feedback Systems

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3355))

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Abstract

Revolutionary advances in technology have generated numerous complex systems that have become integral parts of our socioeconomic environment. The study of such systems – those which contain many interacting parts – is currently attracting considerable attention.

In this paper, the author retraces his personal attempts, over a period of four decades, to develop simple models for adaptation, learning, identification and control using artificial neural networks, and hybrid systems, and goes on to describe how they are providing insights into dealing with complex interconnected systems.

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References

  1. Bellman, R.: Adaptive Processes – A Guided Tour. Princeton University Press, Princeton (1961)

    MATH  Google Scholar 

  2. Parks, P.C.: Lyapunov redesign of model reference adaptive control systems. IEEE Trans. Aut. Control AC-11, 362–367 (1966)

    Article  Google Scholar 

  3. Narendra, K.S., Annaswamy, A.M.: Stable Adaptive Systems. Prentice-Hall, Englewood Cliffs (1989)

    Google Scholar 

  4. Carroll, R., Lindorff, D.P.: An adaptive observer for single-input single-output linear systems. IEEE Transactions on Automatic Control 18, 428–435 (1973)

    Article  MATH  MathSciNet  Google Scholar 

  5. Luders, G., Narendra, K.S.: An adaptive observer and identifier for a linear system. IEEE Trans. Aut. Control 18, 496–499 (1973)

    Article  Google Scholar 

  6. Kreisselmeier, G.: Adaptive observers with exponential rates of convergence. IEEE Trans. Aut. Control 22, 2–8 (1977)

    Article  MATH  MathSciNet  Google Scholar 

  7. Monopoli, R.V.: Model reference adaptive control with an augmented error signal. IEEE Trans. Aut. Control AC-19, 474–484 (1974)

    Article  MATH  Google Scholar 

  8. Narendra, K.S., Lin, Y.-H., Valvani, L.S.: Stable adaptive controller design, part ii: proof of stability. IEEE Trans. Aut. Control AC-25, 440–448 (1980)

    Article  MATH  Google Scholar 

  9. Goodwin, G.C., Ramadge, P.J., Caines, P.E.: Discrete-time multivariable adaptive control. IEEE Trans. Aut. Control AC-25, 449–456 (1980)

    Article  MATH  MathSciNet  Google Scholar 

  10. Morse, A.S.: Global stability of parameter adaptive control systems. IEEE Trans. Aut. Control AC-25, 433–439 (1980)

    Article  MATH  MathSciNet  Google Scholar 

  11. Ioannov, P.A., Sun, J.: Robust Adaptive Control. Prentice Hall, Englewood Cliffs (1996)

    Google Scholar 

  12. Kristic, M., Kannellakopoulos, I., Kokotous, P.V.: Nonlinear design of adaptive controllers for linear systems. IEEE Trans. Aut. Control 39, 738–752 (1994)

    Article  Google Scholar 

  13. Seto, D., Annaswamy, A.M., Ballieul, J.: Adaptive control of nonlinear systems with triangular structure. IEEE Trans. Aut. Control 39, 1411–1428 (1994)

    Article  MATH  Google Scholar 

  14. Narendra, K., Thathachar, M.A.L.: Learning Automata – An Introduction. Prentice-Hall, Englewood Cliffs (1989)

    Google Scholar 

  15. Norman, M.F.: On linear models with two absorbing barriers. J. Math. Psych. 5, 225–241 (1968)

    Article  MATH  MathSciNet  Google Scholar 

  16. Shapiro, I.J., Narendra, K.S.: Use of stochastic automata for parameter selfoptimization with multi-modal performance critera. IEEE Trans. Sys. Sci. Cyber. SSC-5, 352–360 (1969)

    Article  Google Scholar 

  17. Narendra, K.S., Wheeler, R.M.: An n-player sequential stochastic game with identical payoffs. IEEE Trans. Sys. Man Cyber. SMC-13, 1154–1158 (1983)

    MATH  MathSciNet  Google Scholar 

  18. Narendra, K.S., Thathachar, M.A.L.: On the behavior of learning automata in a changing environment with application to telephone traffic routing. IEEE Trans. Sys. Man Cyber. SMC-10, 262–269 (1980)

    Article  MATH  Google Scholar 

  19. Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Trans. Neur. Networks 1, 4–27 (1990)

    Article  Google Scholar 

  20. Cabrera, J.B.D., Narendra, K.S.: Issues in the application of neural networks for tracking based on inverse control. IEEE Trans. Aut. Control 44(11), 2007–2027 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  21. Narendra, K.S., Balakrishnan, J.: Adaptive control using multiple models. IEEE Trans. Aut. Control 42(2) (February 1997)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Yale Center for Systems Science, Yale University, New Haven, CT, USA

    Kumpati S. Narendra

Authors
  1. Kumpati S. Narendra
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Editor information

Editors and Affiliations

  1. Hamilton Institute, NUI Maynooth, Ireland

    Roderick Murray-Smith

  2. Hamilton Institute, NUIM, Ireland

    Robert Shorten

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© 2005 Springer-Verlag Berlin Heidelberg

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Narendra, K.S. (2005). From Feedback Control to Complexity Management: A Personal Perspective. In: Murray-Smith, R., Shorten, R. (eds) Switching and Learning in Feedback Systems. Lecture Notes in Computer Science, vol 3355. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30560-6_1

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  • DOI: https://doi.org/10.1007/978-3-540-30560-6_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24457-8

  • Online ISBN: 978-3-540-30560-6

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