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Automated Modern Control Design

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Final rept. 1 Sep 2001-31 Aug 2003

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The aim of this research is to demonstrate that fuzzy logic may be used to harness human expertise to automatically tune modern control systems, which can lead to higher performing control systems and savings in the time devoted by the control engineer. Designing controllers for which selected system input and output variances are constrained have practical applications to a variety of problems, including control design for flexible structures. This research has demonstrated that a fuzzy algorithm developed by the investigators allows the design of reduced-order, H2 optimal controllers that satisfy bounds on selected system variances. This is the first time that an algorithm has been developed and demonstrated for weight selection in the design of reduced-order controllers. In addition, this research developed a fuzzy algorithm for choosing weights in single-input, single-output H loop shaping control design with multiple time-domain and frequency-domain objectives. As a theoretical extension of the fuzzy weight selection algorithms, this research also developed a fuzzy algorithm for solving inexplicit and undetermined nonlinear systems of the form Fx0, where F Rsuper n right arrow Rsuper m. An inexplicit system is one for which there is no analytical expression for the function Fx. An undetermined nonlinear system is one for which m n. These results have wide applicability since zero-finding problems are prevalent in engineering and science. The report briefly summarizes some of the most important results obtained in this research H2 Optimal Reduced Order Control Design Using a Fuzzy Logic Methodology with Bounds on System Variances Facilitating SISO Design of Multiobjective H Loop Shaping Control Systems and Solution of Inexplicit Systems of Nonlinear Algebraic Equations by Fuzzy Logic. The details of this research can be found in the publications listed at the end of the report.

Subject Categories:

  • Theoretical Mathematics
  • Cybernetics
  • Human Factors Engineering and Man Machine Systems

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