9509539 Poolla The objective of proposed research is to develop computational tools that bridge the gap between classical system identification and robust control, and to understand the fundamental limits of system identification, particularly in the context of feedback control. The proposed research will make the connection between control and identification more transparent, and allow users to make intelligent decisions on identification methodologies, experiment designs, and model validation . The project will have implications in connecting learning and information theory to identification, in general Monte Carlo methods, and in adaptive regulation of time-varying systems. The topics include deterministic methods for model validation, sample-data model validation, statistical methods for model validation, methods for generating robust control compatible uncertainty models from input-output data, and determining fundamental limits of system identification imposed by time-variation and noise. ***
