Neural network modeling and control of cement mills using a variable structure systems theory based on-line learning mechanism

It is well known that the major cause of instability in industrial cement ball mills is the so-called plugging phenomenon. A novel neural network adaptive control scheme for cement milling circuits that is able to fully prevent the mill from plugging is presented. Estimates of the one-step-ahead errors in control signals are calculated through a neural predictive model and used for controller tuning. A robust on-line learning algorithm, based on sliding mode control (SMC) theory is applied to both: to the controller and to the model as well. The proposed approach allows handling of mismatches, uncertainties and parameter changes in the model of the mill. The simulation results from indicate that both the neural model and the controller inherit the major advantages of SMC, i.e. robustness. Furthermore, learning is achieved in a rapid manner.