Constrained minimum variance control using hybrid genetic algorithm – An industrial experience

This paper develops a method for minimum variance control of proportional–integral (PI) controllers in the presence of input stochastic noise, the abatement of which is an important issue in many control systems. The underlying objective is to mitigate the effect of input noise in the process output, subject to process inequality constraints. For this purpose, a hybrid genetic algorithm is used. It combines the genetic operations of selection, crossover, and mutation with Newton search. The developed method is applied in an industrial setting to find the optimal controller parameters of different control loops at Falconbridge Smelter in Sudbury, Canada. The optimal parameters significantly improve the performance of the PI controllers.