Stochastic Adaptive Control: Design and Analysis
{'Code': '07010000', 'Directorate': {'Abbreviation': 'ENG', 'LongName': 'Directorate For Engineering'}, 'Division': {'Abbreviation': 'ECCS', 'LongName': 'Div Of Electrical, Commun & Cyber Sys'}}
{'SignBlockName': 'Radhakisan Baheti', 'PO_EMAI': None, 'PO_PHON': None}
The control engineer is often confronted with the task of designing             controllers for noisy systems whose parameter variation is so severe            that classical or state space based controllers cannot handle it.               This problem provides the material of Adaptive Control Theory.                                                                                                  There are several aspects to Adaptive Control Theory.  The design               problem is to produce algorithms that will adjust control settings in           real time in response to plant variations.  The analysis problem is             to study the behavior and performance of such algorithms.  Analysis             may (and has) lead(led) to design changes.  Analysis involves                   questions of global stability particularly in the presence of                   unmodeled dynamics and performance (e.g. attained tracking error                power and required control power or effort).                                                                                                                    The aim of the work proposed here is to look at these issues in a               stochastic setting.  To design new algorithms it seems useful to                consider modelling the parameter variation, but this immediately                raises a dimensionality problem; a resolution of this based on Factor           Analysis ideas is suggested.  Next is the issue most basic to                   Adaptive Control, namely stability.  Three stochastic tools available           to deal with this are reviewed and likely successful avenues                    explored.  The way in which the unmodeled dynamics problem impinges             is also assessed.  Finally the performance issues can be tackled in a           manner developed in Adaptive Signal Processing through the idea of              misadjustment and excess lag.                                                                                                                                   For good technical reasons, the deterministic and Stochastic Adaptive           Control developments have hitherto been somewhat split, it may now be           possible to begin to bring them together.
{'FirstName': 'Victor', 'LastName': 'Solo', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Victor Solo', 'EmailAddress': None, 'NSF_ID': '000337617', 'StartDate': '07/15/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Johns Hopkins University', 'CityName': 'BALTIMORE', 'ZipCode': '212182608', 'PhoneNumber': '4439971898', 'StreetAddress': '3400 N CHARLES ST', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Maryland', 'StateCode': 'MD', 'CONGRESSDISTRICT': '07', 'CONGRESS_DISTRICT_ORG': 'MD07', 'ORG_UEI_NUM': 'FTMTDMBR29C7', 'ORG_LGL_BUS_NAME': 'THE JOHNS HOPKINS UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}
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[{'Code': '1454', 'Text': 'SYSTEMS THEORY'}, {'Code': '1518', 'Text': 'CONTROL, NETWORKS, & COMP INTE'}]
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