THE APPLIANCE EFFICIENCY ESTIMATION OF PID-REGULATOR PARAMETERS OF NEURAL OPTIMIZER FOR SOLVING OF CONTROL PROBLEM OF METALLURGICAL HEATING PLANTS

The article considers the questions of control scheme with neural optimizer implementation. That optimizer is used for PID-regulator parameters of on-line tuning according to current situation. Three-layer feed-forward neural network was chosen for neural optimizer implementation. That network was on-line trained with the help of back propagation algorithm. The algorithm was modified with several conditions in order to give the neural network the opportunity to consider features of control of heating plants. Energy efficiency estimation of neural optimizer usage for PID-regulator parameters tuning was made during the experiment of cast steel heating in laboratory furnace. Electrical energy saving estimation during the mentioned process was made for control schemes with conventional PID-regulator, PID-regulator with neural optimizer and adaptive PID-regulator, made by Siemens.

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