Dynamic neural networks for output feedback control

Naira Hovakimyan, Rolf Rysdyk, Anthony J. Calise

Research output: Contribution to journalArticlepeer-review


A dynamic neural network is designed to estimate velocities from displacement measurements for a nonlinear system. A linear-in-parameters NN is used for state reconstruction. Conditions are provided under which the estimation error is guaranteed to be ultimately bounded. Subsequently, this observer is integrated into an adaptive controller architecture. The controller is based on model inversion and is augmented with a second learning-while-controlling neural network. Conditions are derived which guarantee ultimate boundedness of all the errors in the combined observer-controller feedback system. Open loop and closed loop simulations for a Van Der Pol oscillator are used to illustrate the results.

Original languageEnglish (US)
Pages (from-to)1685-1690
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - 1999
Externally publishedYes

ASJC Scopus subject areas

  • Chemical Health and Safety
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality


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