Adaptive output feedback control methodology applicable to non-minimum phase nonlinear systems

Naira Hovakimyan, Bong Jun Yang

Research output: Contribution to journalArticlepeer-review


An adaptive output feedback control methodology is developed for a class of uncertain multi-input multi-output nonlinear systems using linearly parameterized neural networks. The methodology can be applied to non-minimum phase systems if the non-minimum phase zeros are modeled to a sufficient accuracy. The control architecture is comprised of a linear controller and a neural network. The neural network operates over a tapped delay line of memory units, comprised of the system's input/output signals. The adaptive laws for the neural-network weights employ a linear observer of the nominal system's error dynamics. Ultimate boundedness of the error signals is shown through Lyapunov's direct method. Simulations of an inverted pendulum on a cart illustrate the theoretical results.

Original languageEnglish (US)
Pages (from-to)513-522
Number of pages10
Issue number4
StatePublished - Apr 2006
Externally publishedYes


  • Adaptive control
  • Neural networks
  • Output feedback

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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