An SPR approach for adaptive output feedback control with neural networks

Anthony J. Calise, Naira Hovakimyan, Moshe Idan

Research output: Contribution to journalConference articlepeer-review


A direct adaptive output feedback control design procedure is developed for highly uncertain nonlinear systems, that does not rely on state estimation. The approach is also applicable to systems of unknown, but bounded dimension. This includes systems with both parametric uncertainties and unmodelled dynamics. This result is achieved by extending the universal function approximation property of linearly parameterized neural networks to model unknown system dynamics from input/output data. The network weight adaptation rule is derived from Lyapunov stability analysis, that guarantees boundedness of the NN weights and the system tracking errors. Numerical simulations of an output feedback controlled Van der Pol oscillator, coupled with a linear oscillator, are used to illustrate the practical potential of the theoretical results.

Original languageEnglish (US)
Pages (from-to)3134-3139
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - 2001
Externally publishedYes
Event40th IEEE Conference on Decision and Control (CDC) - Orlando, FL, United States
Duration: Dec 4 2001Dec 7 2001

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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