Multiparameter self-optimizing systems using correlation techniques

A class of self-optimizing systems which continually alter their parameters to reduce a mean-square performance criterion is described. The change in each parameter is determined from an error gradient in parameter space computed by cross-correlation methods which are independent of signal spectra a...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on automatic control 1964-01, Vol.9 (1), p.31-38
Main Authors: Narendra, K., McBride, L.
Format: Article
Language:English
Subjects:
Adaptive control
Application software
Automatic control
Computer simulation
Differential equations
Error correction
Linear systems
Signal design
Testing
Weight control
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A class of self-optimizing systems which continually alter their parameters to reduce a mean-square performance criterion is described. The change in each parameter is determined from an error gradient in parameter space computed by cross-correlation methods which are independent of signal spectra and require no test signal or parameter perturbation. Applications of this technique to both open-loop and closed-loop systems are included and it is shown that a combination of such self-optimizing systems is a possible solution to the adaptive control problem. Computer simulation results are included to demonstrate the practicality of the proposed systems.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.1964.1105638