519.711.3
Doi: 10.31772/2587-6066-2020-21-2-215-223
NON-PARAMETRIC MULTI-STEP ALGORITHMS FOR MODELING AND CONTROL OF MULTI-DIMENSIONAL INERTIA-FREE SYSTEMS
D. I. Yareshchenko
Siberian Federal University;
26к1, Academician Kirensky St., Krasnoyarsk, 660074, Russian Federation
The paper discusses new classes of models of multidimensional inertia-free systems with a delay in the condi-tions of a lack of a priori information. The subject is multidimensional discrete-continuous processes, the com-ponents of the vector of output variables of which are stochastically dependent in an unknown way. There are also processes, through some channels of which aprior information corresponds simultaneously to both the par-ametric and nonparametric type of source data about the studied process. The mathematical description of such processes leads to a system of implicit nonlinear equations, some of which will be unknown, while others will be known with accuracy to the parameter vector. The main purpose of a model of an object having stochastic de-pendencies of output variables is to find a forecast of output variables with known input variables.
To find the predicted values of the output variables from known inputs, it is necessary to solve a system of im-plicit nonlinear equations. The problem is to solve a system that is actually unknown, when only equations for some channels of a multidimensional system are known. Thus, a rather nontrivial situation arises when solving a system of implicit nonlinear equations under conditions when, in one channel of a multidimensional system, the equations themselves are not in the usual sense, and in others they are known accurate to parameters. Therefore, an object model cannot be constructed using the methods of the existing identification theory because of a lack of aprior information. The purpose of this work is the solution of the identification problem in the presence of a partially-parameterized discrete-continuous process, and despite the fact that the parameterization stage cannot be overcome without additional priori information about the process under study.
The control algorithm for multidimensional processes with dependencies of output variables is a sequential multi-step algorithmic chain that allows finding the control action and bring the object to the desired state.
Computational experiments to study the proposed models and to control multidimensional discrete-continuous processes have shown quite satisfactory results. The article presents the results of computational experiments illustrating the effectiveness of the proposed technology for predicting the values of output varia-bles from known input variables, as well as for managing these processes.
Keywords: multidimensional discrete-continuous process, identification, control, T-models, KT-models.
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