Experiences with model-based controllers based on neural network process models

An earlier paper introduced the MANNIDENT (multivariable artificial neural network identification) approach to process modelling in which approximate linear models determine the topology and initial weights of a neural network and which provides a structure for interpretation of the final model. This paper introduces the MANNCON (multivariable artificial neural network control) system, in which these models are incorporated into model-based controllers. Designs using internal model control (IMC) and direct synthesis control (DSC) as well as a supervisory ANN-based controller are presented. These methods are applied to the task of controlling a non-isothermal CSTR in which a first-order exothermic reaction is occurring. The nonlinear ANN-based controllers perform much better than their linear counterparts in controlling the process over a wide range of conditions. Furthermore, the superivosyr ANN controller showed better robustness properties with respect to disturbance rejection and to plant-model mismatch than the other controllers studied.