Experimental comparative analysis of conventional, fuzzy logic, and adaptive fuzzy logic controllers

Conventional control depends on the mathematical model of the plant being controlled. When this model is uncertain, intelligent controllers promise better performance. We aim in this paper to compare conventional control to fuzzy logic control (FLC) experimentally. This will be achieved by constructing a hardware station comprising a plant and implementing different control algorithms for the same load conditions or disturbances. FLC requires expertise knowledge of the process operation for FLC parameter setting, and the controller can be only as good as the expertise involved in the design. To make the controller less dependent on the quality of the expert knowledge, we investigate different adaptation schemes to compensate for this deficiency and propose a practical adaptive fuzzy logic controller (AFLC).