Performance Analysis of Stochastic Adaptive Controllers
{'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 first is to                produce algorithms that will estimate varying parameters in real time           and then use these estimates to modify the controller settings.  The            next is to study the behavior and performance of such algorithms.               This involves questions of global stability, robustness to model error          (parasitics) and performance (e.g. output power and control signal              power).  The major thrust of this research concerns issues related to           performance analysis of Adaptive algorithms, namely; convergence,               misadjustment and excess lag.  The latter two have yet to be                    investigated for Stochastic Adaptive Control.  To do this, some new             premises are needed.  The premise of parameter convergence is no                longer relevant, instead the almost sure behavior of performance                indices must be directly analyzed.  Finally, it is possible to do               approximate sensitivity calculations for Adaptive Controllers.  It is           expected that this research will yield a better understanding of                stochastic adaptive control and eventually to better control                    algorithms.
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{'Code': '1454', 'Text': 'SYSTEMS THEORY'}
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