Author/Authors :
A.V. Topalov and
O. Kaynak، نويسنده ,
Title Of Article :
Neural network modeling and control
of cement mills using a variable
structure systems theory based
on-line learning mechanism
Latin Abstract :
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.
NaturalLanguageKeyword :
Adaptive control , Intelligent control , neurocontrollers , Variable structure systems
JournalTitle :
Studia Iranica
