Decentralized control of large-scale systems using single Hidden layer neural networks

F. Nardi, N. Hovakimyan, A. J. Calise

Research output: Contribution to journalConference articlepeer-review

Abstract

A decentralized adaptive control design procedure for large-scale uncertain systems is developed using Single Hidden Layer neural networks. The subsystems are assumed to be feedback linearizable and non-affine in the control, and their interconnections bounded linearly by the tracking error norms. Single Hidden Layer neural networks are introduced to approximate the feedback linearization error signal online from available measurements. A robust adaptive signal is required in the analysis to shield the feedback linearizing control law from the interconnection effects. The tracking errors are shown to be uniformly ultimately bounded, and all other signals uniformly bounded. The proposed adaptive algorithm is implemented in simulation to stabilize an interconnected double inverted pendulum.

Original languageEnglish (US)
Pages (from-to)3122-3127
Number of pages6
JournalProceedings of the American Control Conference
Volume4
DOIs
StatePublished - 2001
Externally publishedYes
Event2001 American Control Conference - Arlington, VA, United States
Duration: Jun 25 2001Jun 27 2001

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Decentralized control of large-scale systems using single Hidden layer neural networks'. Together they form a unique fingerprint.

Cite this