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Iterativní formulace cílů řízení v plně pravděpodobnostním návrhu
Jirsa, Ladislav ; Kárný, Miroslav ; Tesař, Ludvík
A control design converts knowledge about the controlled system, constraints and control aims into the controller. Control aims must be quantified in the way compatible to the design. A systematic quantification of the control aims, called aim elicitation, is the least supported step of the design process. We present a solution of this problem within the framework of a fully probabilistic design (FPD) [1]. Any controller modifies the closed-loop behaviour to reach the control aims. The controller is chosen in order to minimize a given loss function. The FPD selects the controller that minimizes the Kullback- Leibler divergence of the joint probability density function (pdf) describing closed-loop behaviour to the ideal pdf. The ideal pdf expresses both the desired closed-loop behaviour and constraints on system inputs. Thus within the FPD, the aim elicitation reduces to the choice of the ideal pdf. For complex multidimensional systems, the task to construct the ideal pdf may represent a nontrivial problem requiring an expert experienced both in practical treatment of the system and theory as well.
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Plně pravděpodobnostní návrh řízení pro gaussovské stochastické systémy
Belda, Květoslav ; Tesař, Ludvík
Control of stochastic systems is generally formulated as a minimization of expected value of a suitably chosen loss function of system inputs, outputs and desired behavior with respect to feedback control strategies. The standard strategies (e.g. Linear Quadratic Gaussian control) choose control actions that make the closed-loop behavior as close as possible to desired one using expected and desired output values. More general approach is to consider complex information on stochastic system behavior by complex probabilistic description. On this approach, fully probabilistic design is based. It uses probabilistic description for characterization of closed-loop of stochastic system and its desired behavior. This paper points out the basic principles of fully probabilistic design and its practical application to the control of Gaussian stochastic systems.
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Toolbox Jobcontrol s GUI
Novák, Miroslav ; Tesař, Ludvík
Jobcontrol is a toolbox merging a user-friendly graphical user interface with a powerful set of utilities for system identification employing Gaussian mixture model. It is implemented as a set of M-scripts and MEX-binary executables for the Matlab computing environment. It suits to the goal of finding a suitable structure for the given data and then it creates the optimal controller by tuning its parameters to fit the user's requirements.
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