999C1a:GAP102/11/0437 - Výsledky hledání - Digitální repozitář

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	Extending Horizon of Finite Control Set MPC of PMSM Drive with Input LC Filter using LQ Lookahead Šmídl, Václav ; Janouš, Š. ; Peroutka, Z. Finite control set model predictive control (FS-MPC) has been shown to be a very effective approach to control of PMSM drives. FS-MPC is a very flexible tool since it can evaluate an arbitrary loss function. However, design of the appropriate loss function for the problem can be a challenge especially when the design input is visible only on the long horizon. An example where this problem becomes apparent is the main propulsion drive of a traction vehicle fed from a dc catenary. Specifically, the catenary voltage is subject to short circuits, fast changes, harmonics and other disturbances which can vary in very wide range. Therefore, the drive is equipped with the trolley-wire input LC filter. The filter is almost undamped by design in order to achieve maximum efficiency and the control strategy needs to secure active damping of the filter to guarantee the drive stability. While it is possible to introduce active damping terms to the loss function, it is hard to predict its properties. Úplný záznam
	Centralized Estimation of Adhesion Loss in Wheel-Rail System Using Variational Bayes and Variational Message Passing Dedecius, Kamil The report deals with centralized estimation of adhesion loss in system wheel-rail. Two variational methods are used for this purpose, namely the variational Bayes and the variational message passing. Úplný záznam
	Approximate Dynamic Programming based on High Dimensional Model Representation Pištěk, Miroslav In this article, an efficient algorithm for an optimal decision strategy approximation is introduced. The proposed approximation of the Bellman equation is based on HDMR technique. This non-parametric function approximation is used not only to reduce memory demands necessary to store Bellman function, but also to allow its fast approximate minimization. On that account, a clear connection between HDMR minimization and discrete optimization is newly established. In each time step of the backward evaluation of the Bellman function, we relax the parameterized discrete minimization subproblem to obtain parameterized trust region problem. We observe that the involved matrix is the same for all parameters owning to the structure of HDMR approximation. We find eigenvalue decomposition of this matrix to solve all trust region problems effectively. Úplný záznam

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