Event-based intelligent control of saturated chemical plant using an endomorphic neural network model

Abstract

An event-based control system with an endomorphic neural network model is designed and realized to control a saturated non-linear plant. The scheme employed in this system is based on an event-based control paradigm previously proposed to control monotonic plants. However, this scheme is different from the previous one in that it can be used to control plants with saturation property. This new scheme may be viewed as a combined method of a time-based diagnosis mechanism in an event-based control system and a state-based control mechanism in a neural network control system. A chemical plant having strong non-linearity and complicated dynamics is controlled using this realized event-based control system. This paper discusses the structure of an event-based controller, the neural network modelling methodology, some related problems, and the simulation results.