Titlebook: Optimization Under Stochastic Uncertainty; Methods, Control and Kurt Marti Book 2020 The Editor(s) (if applicable) and The Author(s), under

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Success/Failure-Driven Random Direction ProceduresIn this section we consider a random direction procedure, cf. G.S. Tarasenko, based on a very simple step length control. At any iteration step . the step length 1. is chosen deterministically according to the following algorithm: . are fixed parameters.

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978-3-030-55664-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Optimization Under Stochastic Uncertainty978-3-030-55662-4Series ISSN 0884-8289 Series E-ISSN 2214-7934

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Optimal Open-Loop Control of Dynamic Systems Under Stochastic Uncertaintyinty, approximate optimal feedback controls under stochastic uncertainties can be obtained by means of open-loop feedback control methods used also in model predictive control under stochastic uncertainty. In order to simplify the notation, the intermediate starting points .. of the open-loop control problems are here abbreviated here just by ...

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Optimal Control Under Stochastic Uncertaintyby dynamical control systems obtained from physical measurements and/or known physical (a priori) laws. The basic control system (input–output system) is mathematically represented by a system of first order differential equations with random parameters: . Here, . is the basic random element taking

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Stochastic Optimization of Regulators of the control system. However, due to variations of material properties, tasks to be executed, modeling errors, etc., the model parameters are not exactly known and given quantities. In addition, the state of the system cannot be observed or measured exactly there are always some observational/mea

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