Systematic design and analysis of fuzzy-logic control and application to robotics,: Part I. Modeling

Abstract

A systematic methodology for synthesis and analysis of fuzzy-logic controllers is proposed in this paper (Part I) and its follow up (Part II) [M.R. Emami, et al., Robotics and Autonomous Systems 33 (2000) 89–108]. A robust model-based control structure is suggested that includes a fuzzy-logic inverse dynamics model and several robust fuzzy control rules. The model encapsulates the knowledge of the system dynamics in the form of IF–THEN rules. The paper focuses on how to obtain this knowledge systematically from the input–output data of a complex system; one that is ill-defined or contains complicated phenomena that are difficult to interpret analytically. All practical steps, from data acquisition to model validation, are illustrated using a four degree-of-freedom robot manipulator. Comparing the results with those of a complete analytical model and a heuristic fuzzy modeling technique illustrates the strength of the proposed methodology in terms of capturing effects that are difficult to model. In the follow-up paper, this model is used in the proposed control structure.