Fuzzy modeling and model based control with use of a priori knowledge

In order to solve the problem of model based control arising from the process model has to be obtained by using small amount and different type of available information, a fuzzy modeling framework has been developed for the utilization of a priori knowledge. The proposed modeling approach transforms the different types of information into the structure of the model (fuzzy rule base), constraints defined on the parameters and variables, dynamic local model or data, and steady-state data or model. This modeling step is followed by an optimization procedure based on these transformed information. The paper describes one element of this framework that was developed to use prior knowledge in constrained adaptation of the rule consequences of Takagi-Sugeno fuzzy models. Experimental results have been obtained for a laboratory setup consisting of two cascaded tanks. It has been shown that by using constrained adaptation, good control performance can be achieved for a nonlinear, time-varying process.