The role of the human in intelligent control practices

Operator´s models are attracting the interest of industry as they relate to monitoring and control of complex engineered processes. Such processes are usually relying upon the expertise and ability of human operators for control purposes; they are often controlled sub-optimally owing to high personnel turnover, lack of fundamental understanding of plant dynamics, inaccuracy or unreliability of manual control actions, etc. Capturing and utilizing the expert´s knowledge effectively and efficiently in an intelligent control framework promises to improve plant operational conditions particularly in the event of large-scale disturbances. Modeling the expert operator´s experience is a perfect semiotic paradigm since the operator employs a variety of informational granules in order to establish an effect-cause relationship and take appropriate action in the event of an emergency. This paper focuses upon the role of the human in intelligent control, means to extract expert knowledge, represent it and employ it for control of complex systems. A neuro-fuzzy implementation strategy is suggested and the model is tested on a pH neutralization process