Active Approaches to Identification and Failure Detection

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0620986 CampbellModern systems often involve both continuous and discrete processes. The discrete nature arises because of events such as discrete time controllers. These systems are called hybrid systems. The desire for increased performance makes consideration of nonlinear behavior necessary. Many classes of problems are most naturally modeled as a system of differential and algebraic equations (DAE). Recently it has been shown that a number of fundamental problems in control including observer design, model identification, and failure detection can be naturally viewed as problems involving DAEs. Intellectual Merit: The proposed research is a major extension of an active failure detection approach to include sampled data, hybrid, implicit and explicit nonlinear systems. Both theory and practical, robust, numerical algorithms will be developed. New theoretical understanding of nonlinear hybrid DAEs and their application to failure detection will result. The results will be applied to applications from areas such as aerospace, mechanical engineering and power systems. Broad Impact: Failure detection is now part of essentially every complex device or process. Failure detection plays a fundamental role in managing costs, promoting efficiency, and protecting the environment. Because of the pervasiveness of both hybrid and nonlinear processes and the need for failure detection, the proposed project will result in substantial advancement in a number of application areas, the training of the next generation of researchers to work on these problems, and contributions to several areas of national need. Among the broad impacts will be improved safety and reliability of manufacturing and transportation systems.