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Příklady odhadu stavu a parametrů pro lineární model s rovnoměrně rozloženými inovacemi
Pavelková, Lenka
In this contribution, state-space model with uniformly distributed innovations is introduced and the Bayesian state estimation proposed. The off-line evaluation of the maximum a posteriori probability (MAP) estimate of unknowns in the linear state-space model with uniform innovations reduces to linear programming (LP). The solution provides either estimates of the noise boundary and parameters or of the noise boundary and states. The on-line estimation is obtained by applying LP on the sliding window, i.e., by considering only the fixed amount, say partial, of the newest last data and states items. By swapping between state and parameter estimations, joint parameter and state estimation is obtained. The use of Taylor expansion for approximation of products of unknowns solves also the joint parameter and state estimation. Simulation studies help to get an insight on the potential and restrictions of these heuristic method. This contribution shares the experimentally gained experience with both these solutions of the joint state and parameter estimation.
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Optimalizace návrhu řízení založená na Monte-Carlo simulaci
Novák, Miroslav ; Pavelková, Lenka
We propose an optimization technique of controller parameters for given system according to defined signal constraints. The quality of the controller is evaluated using Monte-Carlo simulation. The system is considered to be stochastic. The optimization uses sample path method and deterministic quasi-Newtonian method. Capability of proposed technique is illustrated in the experiment.
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