Case studies of optimum filter-controller design in sampled data systems
McMichael, John Coleman
Skezas, George Christ
Strum, Robert D.
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In this paper an investigation is made of the problem of estimating and predicting the states of a linear, discrete, time-invariant, dynamic process which is excited by Gaussian noise and where the observable states are disturbed by Gaussian measurement noise. The concepts of optimum filter design, originally developed by R. E. Kalman, are utilized. Also we have closed the loop on two illustrative examples by determining the optimal control for the plant as a function of the plant's state variables. Here the concepts of a cost performance index and dynamic programming (the latter originally developed by R. Bellman) are employed. The CDC 1604 digital computer, using Fortran 60 programming is utilized in the solution of the optimum filter-controller design.
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