On Stochastic Control and Optimal Measurement Strategies.
MASSACHUSETTS INST OF TECH CAMBRIDGE ELECTRONIC SYSTEMS LAB
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The thesis is concerned with the control of stochastic dynamic systems, with particular emphasis on those which have the property that one can influence the quality or nature of the measurements which are made to effect control. Four main areas are discussed. First, the meaning of stochastic optimality and the means by which dynamic programming may be applied to solve a combined controlmeasurement problem is discussed. Second, a technique is described by which it is possible to apply deterministic methods, specifically the Minimum Principle, to the study of stochastic problems. Third, the methods described are applied to linear systems with Gaussian disturbances to study the structure of the resulting control system. A useful separation property is shown to hold for linear systems with quadratic cost criteria and Gaussian noise. Fourth, several applications are considered. Author
- Statistics and Probability