A predictive thickness control structure and decision about the better control parameter for the cold rolling process through sensitivity factors via neural networks

The single stand rolling mill governing equation is a non-linear function on several parameters (entry thickness, front and back tensions, yield stress and friction coefficient among others). Any alteration in one of them will cause alterations on the rolling load and, consequently, on the outgoing thickness. This paper presents a method to determinate the appropriate adjustment for thickness control considering three possible control parameters: roll gap, front and back tensions. The method uses a predictive model based in the sensitivity equation of the process where the sensitivity factors are obtained by differentiating a neural network previously trained. The method considers as the best control action the one that demands the smallest adjustment. By otherwise, one of the capital issues in the controller design for rolling systems is the difficulty to measure the final thickness without time delays. The time delay is a consequence of the locations of the outgoing sensors that are always placed some distance away from the roll gap. The proposed control system calculates the necessary adjustment based on a predictive model for the output thickness. This model permits to overcome the time delay that exists in such processes. The new structure can eliminate the thickness sensor, usually based on an X-ray detector. Simulation results show the feasibility of the proposed technique.