Abstract
This paper presents a knowledge-based approach to real-time predictive control which is applicable to a large class of industrial process control problems. The class of processes considered are those where dead-time is significant (common in the paper and pulp, mining and chemical process industries), and the approach adopted is an integration of hard algorithms with knowledge-based methods. In the paper, the appropriate hardware and software considerations to achieve the real-time knowledge-based framework are discussed, and the development of the knowledge-base for the predictive controller is presented. The paper also includes the results of real-time experiments for level control in a coupled-tank system.
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