АНАЛІЗ СИСТЕМИ ПАРАЛЕЛЬНОГО НЕЙРОУПРАВЛІННЯ ДИНАМІЧНИМИ ОБ'ЄКТАМИ

Abstract

The article analyzes the effectiveness of the neural network control system, which together with the PIDcontroller implements the principle of parallel control of a dynamic object. As a rule, most industrialfacilities are characterized by non-linear dependencies, the presence of uncontrolled noise anddisturbances, frequent changes in equipment operating modes, and the presence of significant non-linearities. The model of the blowing subsystem of a water-tube steam boiler was used as an object ofresearch. The training of the neural network controller (NMC) and neuroemulator (emulator) was carriedout on the ACS model with a PID controller using the method of expert adjustment of tuning coefficients:proportionality, constant integration and differentiation based on the analysis of the quality indicators ofthe transition process. The change in the values of the object model parameters along the control anddisturbance channels corresponded to the dynamic modes of operation of the steam boiler in the range ofsteam load (25-110%) from the nominal one. The analysis of transient processes obtained on the basis ofcomputer modeling allows us to assert that the trained neural network control system compensates fordisturbances over the entire range of changes in the values of the object parameters along the control anddisturbance channels (simulation of changes in the steam load), as well as when the parameter values ofthe models go beyond the range study sample. Thus, the neural network controller can successfully perform the functions of an adaptive circuit tuned tothe most unfavorable disturbances in the ACS of parallel action by a complex production facility. And theimplementation of a neural network system of parallel action together with typical regulators in thetechnological processes of heat energy can reduce emergency situations associated with frequent changesin the steam load of power units caused by military actions in our country.