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- and closed-loop simulations for a Van Der Pol oscillator are used to illustrate the results.

Original languageEnglish (US)
Pages (from-to)23-39
Number of pages17
JournalInternational Journal of Robust and Nonlinear Control
Issue number1
StatePublished - Jan 2001
Externally publishedYes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Chemical Engineering
  • Biomedical Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


Dive into the research topics of 'Dynamic neural networks for output feedback control'. Together they form a unique fingerprint.

Cite this