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Neural Network Feedback Control: Work at UTA’s Automation and Robotics Research Institute

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This is an outline of research in neural networks for feedback control done since the mid 1990s at the Automation and Robotics Research Institute (ARRI) of The University of Texas at Arlington (UTA). It shows how the developments of Intelligent Control Systems based on neural networks have followed three main generations. This statement provides a short, broad-brush perspective on the development of intelligent neural feedback controllers.

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References

  1. Abu-Khalaf, M., Huang, J., Lewis, F.L.: Nonlinear H2/H-infinity Constrained Feedback Control: A Practical Design Approach Using Neural Networks. Springer, Berlin Heidelberg New York (2006)

    Google Scholar 

  2. Bertsekas, D.P., Tsitsiklis, J.N.: Neuro-dynamic Programming. Athena Scientific, MA (1996)

    MATH  Google Scholar 

  3. Cheng, T., Lewis, F.L., Abu-Khalaf, M.: A neural network solution for fixed-final time optimal control of nonlinear systems. Automatica (2007)

  4. Kanellakopoulos, I., Kokotovic, P.V., Morse, A.S.: Systematic design of adaptive controllers for feedback linearizable systems. IEEE Trans. Automat. Contr. 36, 1241–1253 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  5. Kim, Y., Lewis, F.L.: High-level Feedback Control with Neural Networks. World Scientific, Singapore (1998)

    MATH  Google Scholar 

  6. Lewis, F.L., Ge, S.S.: Neural networks in feedback control systems. In: Kutz, M. (ed) Mechanical Engineer’s Handbook, Instrumentation, Systems, Controls, and mEMS, Book 2, Chapter 19. Wiley, New York (2006)

    Google Scholar 

  7. Lewis, F.L., Syrmos, V.: Optimal Control, 2nd ed. Wiley, New York (1995)

    Google Scholar 

  8. Lewis, F.L., Jagannathan, S., Yesildirek, A.: Neural Network Control of Robot Manipulators and Nonlinear Systems. Taylor and Francis, London (1999)

    Google Scholar 

  9. Lewis, F.L., Campos, J., Selmic, R.: Neuro-fuzzy Control of Industrial Systems with Actuator Nonlinearities. Society of Industrial and Applied Mathematics Press, Philadelphia (2002)

    MATH  Google Scholar 

  10. Lewis, F.L., Dawson, D.M., Abdallah, C.T.: Robot Manipulator Control, 2nd ed. Marcel Dekker, New York (2004)

    Google Scholar 

  11. Werbos, P.J.: Beyond regression: new tools for prediction and analysis in the behavior sciences, Ph.D. Thesis, Committee on Appl. Math. Harvard University (1974)

  12. Werbos, P.J.: Approximate dynamic programming for real-time control and neural modeling. In: White, D.A., Sofge, D.A. (ed) Handbook of Intelligent Control. Van Nostrand Reinhold, New York (1992)

    Google Scholar 

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Correspondence to F. L. Lewis.

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Lewis, F.L. Neural Network Feedback Control: Work at UTA’s Automation and Robotics Research Institute. J Intell Robot Syst 48, 513–523 (2007). https://doi.org/10.1007/s10846-007-9126-0

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