Neural tuner development method to adjust PI-controller parameters on-line

Applying a neural tuner of the PI­controller parameters to control gas heating furnaces

“…Present study is continuation of a larger study into development and application of a neural-network tuner [14][15][16].…”

“…The need for three sets of coefficients is explained by the different character of nonlinearity in a control object during heating and cooling. Functioning on one set of scale leads to the selection of non-optimal parameters and the deterioration in a transition process [15].…”

“…A neural network used in the structure of neural-network tuner represents a three-layer network of direct propagation of signal whose selection is described in detail in [15]. The input layer is represented by four neurons that receive: temperature setpoint, averaged output of the control object, delayed by 1 s, Δt, and output from a PI-controller.…”

“…The input layer is represented by four neurons that receive: temperature setpoint, averaged output of the control object, delayed by 1 s, Δt, and output from a PI-controller. Calculation of Δt coefficient was performed according to [15]. In the experiment described in the paper, we used value Δt=20 s. The hidden layer contains 12 neurons with sigmoidal activation functions.…”

“…Much of the previous research by authors of the present study [14][15][16] was conducted for the model of muffle furnace SNOL 1,6.2,5.1/11-I, shown as (1).…”