The problem of estimating unknown input effects in control systems based on the methods of the theory of optimal dynamic filtering and the principle of expansion of mathematical models is considered. Equations of dynamics and observations of an extended dynamical system are obtained. Algorithms for estimating input signals based on regularization and singular expansion methods are given. The above estimation algorithms provide a certain roughness of the filter parameters to various violations of the conditions of model problems, i.e. are not very sensitive to changes in the a priori data.
The article deals with the formation of stable algorithms for the system synthesis for the stabilizing uncertain dynamic objects based on the method of local optimization in the presence of approximate mathematical models. The article analyzes the issues of building adaptive control systems using the concept of roughness, taking into account the assessment of the maximum allowable discrepancy between the object and its model. Some of the most constructive algorithms for determining pseudo-inverse matrices are given. When calculating the pseudo-inverse matrix of the control object, modified QR decomposition algorithms are used, obtained by deleting or assigning a column. The obtained algorithms allow us to conclude that the stabilization systems can be built on the basis of the local optimization method in the presence of approximate mathematical models. At the same time, it turns out that the asymptotic stability, i.e., the limited output with a limited input, can be achieved with sufficiently rough estimates of the object parameters and external perturbations using regular methods.
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