This article is cited in 1 paper

Conditionally-optimal filtration and control for stochastic systems with random parameters and unsolved derivatives

I. N. Sinitsyn

Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119133, Russian Federation

Abstract: Methodological support for synthesis of conditionally: optimal filtering and control for continuous, discrete, and continuous-discrete stochastic systems with unsolved derivatives (StS USD) and random parameters is developed. A survey of conditionally-optimal filtering (COF) and conditionally optimal control (COC) is presented. It is supposed that random parameters are described by multicomponent integral canonical expansions (MC ICE). Synthesis of COF and COC is based on Pugachev's COC concepts. Special attention is paid to the three COC typical problems in the case of local criteria. Applications: nonlinear correlational estimation of potential and instrumental accuracy of COF and COC at nonstationary impact disturbances in StS USD. An illustrative example is given. Future research is connected with effects of disturbances accumulation, drifts, and excurtions based on finite dimension distribution of stochastic processes.

Keywords: conditionally optimal control (COC), conditionally optimal filtering (COF), multicomponent integral canonical expansions (MC ICE), random parameters, stochastic systems with unsolved derivatives (StS USD).

Received: 13.11.2023

DOI: 10.14357/08696527240201