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" Adaptive Control of Discrete-time Systems Using Multiple Models "


Document Type : Latin Dissertation
Language of Document : English
Record Number : 151468
Doc. No : ET23260
Main Entry : Cheng Xiang
Title Proper : Adaptive Control of Discrete-time Systems Using Multiple Models
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : The control of dyamical systems in the presence of large uncertainties is of greatinterest and also quite a challenge at the present time. lL1dtipIe model based on adaptive control has recently emerged as an efficient scheme to deal with such problems.In this thesis, we take the fim step to study the important problem of stability andconvergence of adaptive control of linear time-invariant discretetime systems usingmultiple models.Both the deterministic (noise free) case, and the stochastic case when random dis-turbances are present, are discussed. Based on the prediction errors of a finite num-ber of bed and adaptive identification models, a procedure is outlined for switchingbetween a finite number of controllers to improve performance. The principal con-tributions of the thesis are the proof of gIobaI stability of the overalI system and theconvergence of the tracking error signal to zero in the deterministic case, and theproof of convergence of the minimum variance control problem in the stochastic case.Extensions of the stability analysis to the multiple-input multipleoutput (hml1:O)systems are also presented. Some design considerations are discussed toward the endof the thesis, and computer simulation results are dso included wherever available torompIement the theoretical resdts.....-....,....-...,..tested for theQ1 PC1 bus cardBoth these projects mere sofixare des elopment efforts tonards contributing to dlfferentaspects of Roboucs and lZ1echatronics projects m the Controls and Roboucs Group..
Subject : Electericl tess
: برق
electronic file name : TL46496.pdf
Title and statement of responsibility and : Adaptive Control of Discrete-time Systems Using Multiple Models [Thesis]
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TL46496.pdf
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