Abstract
This paper presents a simple method for minimum structured process modelling via a rule-based technique. Under the assumption that the processes are stable, step input signals are applied and step responses data are collected. Six different types of minimum structured models are then defined. They are Monotone Response (MR), Undershoot Monotone Response (UMR), Oscillatory Response (OR), Undershoot Oscillatory Response (UOR), Odd Undershoot Monotone Response (OUMR), and Odd Undershoot Oscillatory Response (OUOR). A classifier is built which, for a given step response, generates the information about the type of response. A proposed minimum structured model is then obtained. Different minimum structured models are used to achieve the initial fitting for the given response. This leads to the rough tuning of model parameters. Finally, a rule-based fine tuner is constructed and used to find out the accurate parameters of the proposed model. Desirable results are obtained when the method is applied to the modelling of the machine direction weight profile in a paper-making process and the speed control system of a hydraulic turbine generator.
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