Development of model-referenced fuzzy adaptive control

Abstract

This paper outlines the development of three different types of model-referenced adaptive control and then evaluates their performance through computer simulation. Specifically, conventional model-referenced adaptive control is used as the basis of comparison of the performance of two knowledge-based techniques. Fuzzy logic is used in the development of the knowledge base and for decision making, in the two techniques. In one knowledge-based technique, the parameters of a low-level direct digital controller are adapted so that the system tracks a reference model. In the other knowledge-based technique, the reference input to the system is adapted. Simulation studies are carried out for the three techniques, as applied to a simple nonlinear servomotor and load system. Results indicate that the knowledge-based adaptive techniques can outperform the conventional technique in specific situations.