SYNERGETIC CONTROL OF NONLINEAR DYNAMIC OBJECTS

Considers the synthesis of effective control algorithms for a chemical reactor, which is the main one in the production of butyl alcohol. The method based on synergetic approach for transferring the apparatus from one capacity to another (specified) maintaining the required quality of the target component is proposed in order to ensure the stabilization of the concentration of target component at the outlet of the reactor under the conditions of uncertain disturbances affecting to an object. A mathematical model of the process based on a stoichiometric matrix taking into account the physicochemical properties of the process is developed. The control algorithm was synthesized by the method of analytical design of an aggregate controller that provides the necessary property of a control system for a chemical reactor. The mathematical model of the process serves as the basis for the selection of attractors of phase variables. The proposed method for synthesizing a control system based on a synergistic approach has made it possible to better stabilize the temperature regime of the process and the concentration of chemical reagents

DEVELOPMENT OF A TECHNOLOGICAL MODELING SYSTEM FOR REFINING PROCESSES

The article considers with the creation of a modeling system that allows the formation the dynamics of the technological process. Taking into account its physical and chemical properties. To solve this issue, the application of a semantic network is proposed, which ensures the aggregation of process models based on their compatibility. The proposed approach is implemented for dehydrogenation parameters, which showed the effectiveness of the proposed approach

Neural network model of decision making in electric power facilities under conditions of uncertainty

The article is devoted to the issue of creating a mathematical model of the problem of making management decisions in electric power facilities based on modern intelligent technologies, which makes it possible to take into account the influence of various factors on the operating modes of the power system. A systematic approach to describing processes in the mathematical language of the theory of fuzzy sets is proposed. To solve the problem of controlling the operating modes of the power system, a neurofuzzy network has been developed that combines the algorithms of Takagi-Sugeno fuzzy inference, as well as a recurrent neural network. An adaptive learning algorithm based on the Frechet method is proposed for training a neural network. The analysis of the efficiency of the fuzzy control model under the conditions of various modes of functioning of the local power system is carried out