Neural adaptive control for multivariable nonlinear processes

Neural-network techniques are investigated in an application to the identification and subsequent on-line control of a process exhibiting nonlinearities and typical disturbances. The method proposed consists from a novel identification technique based on extended memory adaptation (EMA) and an efficient implementation of the predictive control based on a nonlinear programming method. An forced circulation evaporator was chosen as a realistic nonlinear case study for the techniques discussed in the paper.