Back-propagation neural networks for nonlinear self-tuning adaptive control

Fu-Chuang Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

238 Scopus citations


A back-propagation neural network is applied to a nonlinear self-tuning tracking problem. Traditional self-tuning adaptive control techniques can only deal with linear systems or some special nonlinear systems. The emerging back-propagation neural networks have the capability to learn arbitrary nonlinearity and show great potential for adaptive control applications. A scheme for combining back-propagation neural networks with self-tuning adaptive control techniques is proposed, and the control mechanism is analyzed. Simulation results show that the new self-tuning scheme can deal with a large unknown nonlinearity.

Original languageEnglish
Pages (from-to)44-48
Number of pages5
JournalIEEE Control Systems Magazine
Issue number3
StatePublished - 1 Apr 1990


Dive into the research topics of 'Back-propagation neural networks for nonlinear self-tuning adaptive control'. Together they form a unique fingerprint.

Cite this