The authors discuss the combination of an Artificial Neural Network (ANN) with analytical models to improve the performance of the prediction model of finishing rolling force in hot strip rolling mill process. The suggested model was implemented using Bayesian Evidence based training algorithm. It was found that the Bayesian Evidence based approach provided a superior and smoother fit to the real rolling mill data. Completely independent set of real rolling data were used to evaluate the capacity of the fitted ANN model to predict the unseen regions of data. As a result, test rolls obtained by the suggested hybrid model have shown high prediction quality comparatively to the usual empirical prediction models
Real systems have in general significant characteristics such as high-order, nonlinearities, deadtime, etc. and they can be affected by noise, load disturbances and other environment conditions that cause parameter variations and sudden modifications of the model structure. One of the main difficulties in the tuning of the PID parameters is to address at the same time different control specifications. In particular, achieving a high load disturbance rejection performance generally results in an aggressive tuning that provides a too oscillatory set-point step response. A fuzzy inference system is adopted to determine the value of the weight that multiplies the set-point for the proportional action. This contribution introduces genetic algorithms to change the shapes of the membership of the fuzzy controller by changing their basic parameters.
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