Discrete and sampled-data stochastic control problems with complete and incomplete state information |
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Authors: | A. Johnson |
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Affiliation: | (1) Kramers Laboratory, Faculty of Applied Physics, Delft University of Technology, Prins Bernhardlaan 6, 2628 BW Delft, The Netherlands |
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Abstract: | An unconstrained stochastic optimization problem involving a discrete-time linear process with a normally distributed initial condition and subject to additive gaussian state and measurement noise is formulated in terms of a quite general finite horizon, discrete-time quadratic cost criterion and solved when there is either complete or incomplete state information. It is shown that both the stochastic sampled-data optimal tracker and the stochastic sampled-data optimal regulator are special cases of this problem. A breakdown of the minimum cost for both sampled-data controllers is given. |
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Keywords: | Optimal control Stochastic control Sampled-data controllers Digital control Optimal regulators Optimal tracking |
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