A fuzzy controller with various T-norms applied in robot navigation

Abstract

Fuzzy set theory and fuzzy logic are the convenient tools for handling uncertain, imprecise, or unmodeled data in intelligent decision-making systems. The utility of fuzzy logic in system controls domain is presented in the context of a mobile robot navigation control application. The Takagi-Sugeno controller is a fuzzy model capable of approximating a wide class of nonlinear systems by decomposing the input space into several partial fuzzy subspaces and representing the output space with a linear equation. The output control action is obtained from the rule-base and a set of crisp inputs. A Takagi-Sugeno type Fuzzy Logic Controller (FLC), to work with crisp data, intervals and fuzzy sets inputs, is proposed in connection with a mobile robot navigation model. The model also works with a set of t-norms, and for any t-norm an output value is obtained. Finally, these outputs are combined to obtain the overall output of the system.