Research Initiation Award: Development of a Systematic Approach to Uncertainty Modeling for Robust Control Design

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The objective of this proposal is to develop a systematic approach to uncertainty modeling from experimental data for robust control. In the past decade, theoretical developments in robust control have lead to more rigorous and applicable design techniques which allow the explicit incorporation of plant uncertainty into the problem formulation ı1,2,3,4!. These developments provide a fresh approach that can be exploited to control complex systems and achieve performance specifications which were unthinkable several years ago. The key link currently missing between these theoretical developments and their application in industry is a systematic approach to the generation of performance and uncertainty descriptions. The importance of accurate characterizations of uncertainty descriptions was vividly shown in the design of controllers for an experimental flexible structure ı5!. Controllers synthesized for "accurate" descriptions of the plant uncertainty yielded significant improvements in system performance when compared with other less accurate uncertainty descriptions. The proposed research will develop a methodology for attributing the error between theoretical system model and experimental data to uncertainty and noise model in a linear fractional framework which is compatible with the structure value mu-methodology. These same techniques can also be employed to develop performance descriptions in for this framework. The mu-framework will then be used to analyze and synthesize complex system controllers with these new performance and uncertainty descriptions. The improved modeling of a physical system for the purpose of control design will provide a two fold benefit: improved robustness properties and closed-loop system performance of the controller.