Fine Control of Monotonic Systems using a Global Self-Learning Adaptive Fuzzy Controller

摘要

The goal of this paper is to achieve real time control of a monotonic system which, in general, may be non-linear and whose differential equations are unknown. We assume that there is no model of the plant available so there cannot be any offline pre-training of the main controller parameters. We propose a both adaptive and self-learning algorithm capable of starting from a "void" fuzzy controller and, in real time, optimizing the fuzzy controller's rules (both antecedents and consequents) in order to translate the state of the plant to the desired value in the shortest possible time.