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Optimal Regulation of Stochastic Linear Systems with Adjustable Parameters,
CALIFORNIA UNIV IRVINE SCHOOL OF ENGINEERING
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The problem of optimal regulation for stochastic linear systems with adjustable plant parameters is examined and posed as a nonlinear programming problem. A computational procedure, built around the Generalized Reduced Gradient algorithm, is developed to solve the associated plant-controller design problem. The procedure is illustrated via a lateral autopilot design in which the quality of regulation is improved by approximately 18 over that achievable with a nominal fixed plant. Author
APPROVED FOR PUBLIC RELEASE