Performance-oriented antiwindup for a class of linear control systems with augmented neural network controller

G Herrmann, MC Turner, I Postlethwaite

Research output: Contribution to journalArticle (Academic Journal)peer-review

19 Citations (Scopus)

Abstract

This paper presents a conditioning scheme for a linear control system which is enhanced by a neural network (NN) controller and subjected to a control signal amplitude limit. The NN controller improves the performance of the linear control system by directly estimating an actuator-matched, unmodeled, nonlinear disturbance, in closed-loop, and compensating for it. As disturbances are generally known to be bounded, the nominal NN-control element is modified to keep its output below the disturbance bound. The linear control element is conditioned by an antiwindup (AW) compensator which ensures performance close to the nominal controller and swift recovery from saturation. For this, the AW compensator proposed is of low order, designed using convex linear matrix inequalities (LMIs) optimization.
Translated title of the contributionPerformance-oriented antiwindup for a class of linear control systems with augmented neural network controller
Original languageEnglish
Pages (from-to)449 - 465
Number of pages17
JournalIEEE Transactions on Neural Networks
Volume18 (2)
DOIs
Publication statusPublished - Mar 2007

Bibliographical note

Publisher: IEEE Neural Networks Society

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