Title

Modified State Observer based Decentralized Neuroadaptive Controller for Large Scale Interconnected Uncertain Systems

Abstract

A decentralized neural network controller design for large scale system with multiple uncertainties is presented in this paper. Controller synthesis relies primarily on an observer design, called the modified state observer (MSO). The uncertainties in the system are calculated online with the MSO. The MSO formulation of uncertainty estimation has two tunable gains which allow for fast estimation of the uncertainties and yet avoid high frequency oscillations that are usually observed with typical model reference adaptive controllers. Lyapunov analysis is used to show the stability of the system. Efficacy of the proposed controller is demonstrated using a bench mark numerical example.

Meeting Name

2018 Annual American Control Conference, AAC (2018: Jun. 27-29, Milwaukee, WI)

Department(s)

Mechanical and Aerospace Engineering

Comments

This research was supported in part by the National Aeronautics and Space Administration under Grant NNX15AM51A

International Standard Book Number (ISBN)

978-1-5386-5428-6

International Standard Serial Number (ISSN)

2378-5861

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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