Application of an improved universal learning network internal model control to a continuous stirred tank reactor system

Through the training of time delays on the branches of a universal learning network (ULN) to the optimal values,the modeling precision for a continuous stirred tank reactor (CSTR)—a complicated nonlinear system with a large lag—was greatly improved. Furthermore,an improved ULN internal model control method (improved ULN-IMC) based on fuzzy control theory has been proposed. In simulations of a CSTR using this new method,the tracking and fixed set-point control when subjected to an external disturbance showed good performances.