Intelligent control of a cryogenic cooling plant based on blackboard system architecture

Intelligent system techniques have been rapidly assimilating into process control engineering, with many applications reported in the last decade. Intelligent control is bringing a new perspective as well as new challenges to process control. In this paper, a software architecture for a Blackboard for Integrated Intelligent Control Systems (BIICS) is described. The system is designed to simultaneously support multiple heterogeneous intelligent methodologies, such as neural networks. expert systems, fuzzy logic, neural networks and genetic algorithms. It will be shown how such methodologies can be readily assimilated into the software architecture. The BIICS system represents a multi-purpose platform for design and simulation of intelligent control paradigms for different kinds of processes. Currently the system utilizes intelligent control techniques (neuro-fuzzy and genetic optimization) for controlling a cryogenic plant used for superconductor testing at temperatures below 100 K.