Stochastic Adaptive Estimation and Control
{'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}
This research concerns several basic problems in the area of            stochastic adaptive estimation and control.  The adaptive control               problem (with estimation incorporated) is that of designing a feedback          controller (algorithm) which minimizes a given performance criterion or         tracks some given reference signal when the process model contains              unknown (due to modeling difficulties) or changing (due to wear,                temperature variations, etc.) parameters.  The adaptive control                 algorithm in the feedback loop attempts to actively reduce the parameter        uncertainty by performing on line parameter estimation and then using           these updated estimates to continuously reconfigure or retune the               controller.  The proposed research represents innovative approaches             toward the adaptive estimation and control of systems which are                 nonlinear and/or involve incomplete state observations.  The problems           being considered are more difficult than problems solved to date.  In           particular, this research should result in a deep understanding of the          adaptive estimation and control of Markov chains with incomplete                observations;  such problems arise in the control of computer                   communication networks, as well as in quality control, maintenance,             replacement, and repair of industrial processes.  A thorough                    investigation of the adaptive control of stochastic bilinear systems is         also proposed; such dynamic systems contain terms which are bilinear in         the state and the control.                                                              Professor Marcus is well known for his work on stochastic               control.  The University of Texas at Austin provides a fine research            environment for this project.  This award renews research previously            supported under NSF Grant ECS-8412100.
{'FirstName': 'Steven', 'LastName': 'Marcus', 'PI_MID_INIT': 'I', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Steven I Marcus', 'EmailAddress': 'marcus@isr.umd.edu', 'NSF_ID': '000367593', 'StartDate': '09/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'University of Texas at Austin', 'CityName': 'AUSTIN', 'ZipCode': '787121139', 'PhoneNumber': '5124716424', 'StreetAddress': '110 INNER CAMPUS DR', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Texas', 'StateCode': 'TX', 'CONGRESSDISTRICT': '25', 'CONGRESS_DISTRICT_ORG': 'TX25', 'ORG_UEI_NUM': 'V6AFQPN18437', 'ORG_LGL_BUS_NAME': 'UNIVERSITY OF TEXAS AT AUSTIN', 'ORG_PRNT_UEI_NUM': None}
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[{'Code': '145400', 'Text': 'SYSTEMS THEORY'}, {'Code': '914500', 'Text': 'SPECIAL PROGRAMS-RESERVE'}]
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1986&All=true', 'xml': '8617860.xml'}