Adaptive output feedback control of nonlinear systems using neural networks

Anthony Calise, Naira Hovakimyan, Hungu Lee

Research output: Contribution to journalConference articlepeer-review


An adaptive output feedback controller design procedure for uncertain nonlinear systems is developed which avoids the use of state estimation. To achieve this goal three separate problems are addressed independently: controller design, derivation of parameter update laws and approximate mapping of an unknown dynamic function from its input/output history. To handle the uncertainty, the controller, in the form of a dynamic compensator, is augmented by a single hidden layer (SHL) neural network that adjusts on-line for unknown nonlinearities. The parameter update laws for a SHL neural network are derived from stability analysis. Simulations illustrate the theoretical results.

Original languageEnglish (US)
Pages (from-to)3153-3157
Number of pages5
JournalProceedings of the American Control Conference
StatePublished - 2000
Externally publishedYes
Event2000 American Control Conference - Chicago, IL, USA
Duration: Jun 28 2000Jun 30 2000

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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