On-Line Learning Based on Adaptive Similarity and Fixed Size Rule Base

In this paper a methodology is developed to control linear and nonlinear processes using a fuzzy approach with the main assumption that the output of the process is monotone with respect to the input. Beginning with an empty rule base, a fuzzy model is on-line built. The rule base has a fixed number of rules determined à priori and not depending on the complexity of the process. The controller experiences a learning phase during which it learns how to control the process, that is repeated whenever there is some change in the process behaviour. The inference and defuzzification mechanisms have their background on the Fuzzy Equality Relations Theory, using an adaptive degree of similarity. The proposed controller was successfully applied in simulation for linear and non-linear systems and practical essays were made on a real nonlinear thermal process, for both the regulation and the tracking problem.