Extensional fuzzy logic controllers for uncertain systems

Abstract

First, the possibility of using non-singleton FLS to better handle sensor noise is investigated. The fuzzification strategy is designed to have minimal impact on the system dynamics and to reduce the steady-state fluctuations caused by the presence of noise. In order to handle parameter uncertainty, a type-2 fuzzy PI controller whose control surface is bounded based on the uncertainty is constructed to control systems with uncertain but bounded parameters. An adaptive algorithm to obtain variable centroids is proposed to generate a suitable output surface within the pre-determined control surface range to maintain the desired performance.Finally, by utilizing the extra dimension in the type-2 fuzzy sets, an on-line learning scheme is proposed for a type-2 fuzzy-neural control systems. The objective is to investigate the capability of the extra degrees of freedoms in the type-2 FLS in modelling complex input-output relationship.