The coordinating control system by drives of the robot-manipulator is presented in this article. The purpose of the scientific work is the development and research of the new algorithms for parametric synthesis of the coordinating control systems. To achieve this aim it is necessary to develop the system generating the required parametric synthesis algorithms and performing the necessary procedures according to the generated algorithm. This scientific work deals with the synthesis of Petri net in the specific case with the automatic generation of Petri nets.

В настоящей работе представлен определенный этап разработки интеллектуальной системы, связанной с автоматическим синтезом сетей Петри. Рассматривается определенная архитектура искусственной нейронной сети, которая положена в основу интеллектуальной системы, направленной главным образом на формирование алгоритмов настройки координирующих систем автоматического управления. Особенность функционирования рассматриваемой архитектуры нейронной сети заключается в том, что в любой момент времени может быть активен только один нейрон из определенного количества возможных для активизации других нейронов сети. В работе представляется алгоритм настройки данной нейронной сети, который связан с инцидентной матрицей формируемой сети Петри. При этом формируемая сеть Петри представляет алгоритм настройки координирующей системы автоматического управления. В заключительной части работы представлена разработанная в программной среде MATLAB/Simulink система, формирующая инцидентную матрицу сети Петри на базе функционирования нейронной сети. Отражается визуализация процесса формирования сети Петри, представляющей алгоритм настройки системы управления. Данная визуализация дает возможность представить результат формирования алгоритма, тем самым позволяет определить специалисту, при необходимости, нужную корректировку данного алгоритма.

The process of automated tuning for the coordinating automatic control system is considered in this paper. This process of tuning for the coordinating control system is linked to the automatic synthesis of Petri nets based on functioning of the artificial neural network. Thereby, we can automate the process of tuning and synthesis of system models and also solve the urgent task linked to the minimization of tuning time for the multilevel control systems. The purposes of the scientific work are time reduction of the tuning and automatization of the tuning for the multilevel coordinating systems of the automatic control. In order to achieve this purpose in the MATLAB \ Simulink software environment it is necessary to devel- op the system for automated tuning of the regulators of various levels for the coordinating automatic control system. The application of artificial neural network with automatic synthesis of Petri nets allows to introduce intelligent technology in the automated tuning system. In this work we have presented the corresponding block diagrams of considered automated tuning system and the principles of its functioning. The certain principle of the formation of Petri nets is proposed. These Petri nets represent the algorithms of tuning in the systems for analysis the corresponding processes. The formation of the composition in the scheme from Petri net during the functioning of the artificial neural network is presented in the paper. The results of experiment are presented in the final part of this work. This time characteristics of the pro- cess of setting up for the coordinating automatic control system of foodstuffs cooling in tunnel chamber. The experiments were conducted in the Matlab 2012a environment. Based on the results of the experiment we have depicted the process of synthesis of the Petri net representing the system tuning algorithm. The performed experiments have showed the principal suitability of the automated search system for the settings of the regulators of various levels of the coordinating control system. The technique of automatic synthesis of Petri nets based on the functioning of artificial neural networks has obtained the further devel- opment while performing the approved task in the scientific paper.

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Context. The important task was solved during the scientific research related to the development of the methods for automatic synthesis of Petri nets while tuning up of the coordinating automatic control systems. The importance of development of these methods is due to the evolution of intelligent systems. These systems provide the automation of labor intensive processes in the particular case this is the tuning of the certain type of complex control systems. Objective. The purpose of the scientific work is to minimize the time and automation of process in tuning of the multilevel coordinating automatic control systems. Method. The principle of automatic synthesis of Petri nets and the implementation of certain algorithms for tuning complex control systems based on the functioning of an artificial neural network are proposed. The mathematical description of the method for changing the coefficients in neural connections of network in the synthesis of Petri nets is presented. Results. The experiments were conducted in the Matlab\Simulink 2012a environment. These experiments were bound to the joint functioning of an artificial neural network and Petri nets. The functioning of Petri nets was presented in the Matlab \ Simulink environment using Statflow diagrams. As a result of the experiments we have obtained the temporal characteristics of the functioning of artificial neural network providing the composition of Petri nets. The fundamental suitability of using artificial neural network to provide the automatic composition of Petri nets was determined on the basis of analysis of temporal characteristics. Conclusion. The problem linked to the development of system for the joint functioning of neural network and Petri nets for the formation of algorithms and sequential calculations was solved in this work. Thus the method of automatic synthesis of Petri nets and the method of developing of the certain algorithms based on the functioning of a neural network were further developed.