The Application of GA-RBF Neural Network Generalized Predictive Control Strategy in Circulating Fluidized Bed Unit

Abstract

Circulating fluidized bed boiler, with heat storage capacity, nonlinear, large time delay, multi-variable and strong coupling characteristics, is a complex quantity control system. It is difficult to establish accurate model when the parameters of control object is uncertainty because of all disturbances. This article uses one way to identify model based on genetic algorithm and RBF network combined with generalized predictive control strategy, through online rolling optimization and feedback correction, to achieve predictive control. The results of the simulation show that it has strong robustness when the load condition changes, the parameter changes or lag affects.