Switching controllers based on neural network estimates of stability regions and controller performance

Enrique D. Ferreira, Bruce H. Krogh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

This paper presents new results on switching control using neural networks. Given a set of candidate controllers, a pair of neural networks is trained to identify the stability region and estimate the closed-loop performance for each controller. The neural network outputs are used in the on-line switching rule to select the controller output to be applied to the system during each control period. The paper presents architectures and training procedures for the neural networks and sufficient conditions for stability of the closed-loop system using the proposed switching strategy. The neural-network-based switching strategy is applied to generate the switching strategy embeded in the SIMPLEX architecture, a real-time infrastructure for soft on-line control system upgrades. Results are shown for the real-time level control of a submerged vessel.

Original languageEnglish
Title of host publicationHybrid Systems
Subtitle of host publicationComputation and Control - 1st International Workshop, HSCC 1998, Proceedings
EditorsThomas A. Henzinger, Shankar Sastry
PublisherSpringer Verlag
Pages126-142
Number of pages17
ISBN (Print)3540643583, 9783540643586
DOIs
StatePublished - 1998
Externally publishedYes
Event1st International Workshop on Hybrid Systems: Computation and Control, HSCC 1998 - Berkeley, United States
Duration: 13 Apr 199815 Apr 1998

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1386
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Hybrid Systems: Computation and Control, HSCC 1998
Country/TerritoryUnited States
CityBerkeley
Period13/04/9815/04/98

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