Neural-net computing and the intelligent control of systems

Yoh Han Pao, Stephen M. Phillips, Dejan J. Sobajic

Research output: Contribution to journalArticlepeer-review

204 Scopus citations


In this article, we are concerned with neural-nets which can learn to control systems in accordance with a guiding intent, and can also learn how to formulate that control strategy or intent. The overall task of systems control is viewed as being carried out by four components, these being the predictive monitoring net, the control action generator net, the objective function net and the optimization net. This approach and perspective are described and illustrated in this article. In our examples, we show that systems identification can indeed be achieved in the presence of noise and that optimal control can be formulated in a learning mode, by neural nets.

Original languageEnglish (US)
Pages (from-to)263-289
Number of pages27
JournalInternational Journal of Control
Issue number2
StatePublished - Aug 1992
Externally publishedYes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications


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