Modeling of a neural network-based system for identification and control of technical object parameters


  • Mykhailenko Vladyslav
  • Kamienieva Alla
  • Stukalov Segii
  • Zui Oksana


automatic control system; neural network controller, PID-regulator; boiler; transitional process


The article analyzes the effectiveness of a neural network control system for main-taining the pH level in the feedwater of a steam boiler. An intelligent control system im-plements the principle of reverse error propagation through a neural emulator. The sub-system model of steam boiler water tube blowing was used as the research object. The neural network controller and neural emulator were trained on a control system model with a PID controller using the expert correction methodology of tuning coefficients: proportionality, integration constant, and differentiation based on the analysis of tran-sient process quality indicators. The analysis of the transient processes obtained from simulation modeling allows us to state that the trained neural network control system successfully compensates for disturbances over a wide range of changes in the object's parameter values via control channels and disturbances (simulating changes in steam load).


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