Значительное повышение технических возможностей беспилотных летательных аппаратов (БПЛА) создает условия для расширения их использования в областях, где возможности человека ограничены. Такие приложения, как мониторинг объектов в труднодоступных местах, экстремальные спортивные ралли, спасательная операция, требуется наблюдение за объектами в крупных регионах и сложных условиях. Для обеспечения высокого уровня автономности БПЛА, необходимо решить три основные задачи: интеллектуальное планирование миссий, распознавание объектов в полете, модификация маршрутов полета в соответствии с меняющимися условиями. В статье представлены алгоритмы для решения этих задач на основе методов искусственного интеллекта.

This paper presents a matrix inequality approach to sensor fault estimation in presence of random input disturbance with unknown covariance. The input is supposed to be a correlated stationary Gaussian noise with bounded mean anisotropy. The quality criterion is defined as anisotropic norm of the system. Anisotropic norm of the system defines gain from input disturbance with bounded mean anisotropy to output. The main contribution of the paper is sensor fault estimator design that allows to estimate sensor fault with guaranteed anisotropybased disturbance attenuation level. Numerical example is given.

The problem of lateral motion stabilization is analytically solved for the fourth-order model of aircraft motion using the method of output signal control synthesis that is based on the control object decomposition. Numerical simulation data are presented.

We obtain and discuss new results related to control design for bilinear systems subjected to arbitrary bounded exogenous disturbances. A procedure for the construction of the stabilizability ellipsoid and the domain of stabilizability for bilinear control systems is proposed and its efficiency is proved. This problem is solved both in continuous and discrete time. The main tool is the linear matrix inequality technique. This simple yet general approach is of great potential and can be widely generalized; for instance, to various robust statements of the problem.

This article concerns the recovery of the operators by noisy information in the case that their norms are defined by integrals over infinite intervals. We study the conditions under which the dual extremal problem (often nonconvex) can be solved using the Lagrange function minimality condition.