Fuzzy Models of Dynamical Systems

Abstract

Model-based engineering tools require the availability of suitable dynamical models. Consequently, the development of a suitable nonlinear model is of paramount importance. Given the high expectations of fuzzy models in the area of identification and control, it becomes necessary to analyze and extract control-relevant information from fuzzy models of dynamical processes. Hence, in this chapter after an introduction to the data-driven modeling of dynamical systems, the following characteristics of TS fuzzy models are analyzed:

• Fuzzy models of dynamical systems

• State-space realization of the model

• Prediction of the equilibrium points

• Stability of the equilibrium points

• Extraction of a linear dynamical model around an operating point Based on this analysis, new fuzzy model structures

• Hybrid F\izzy Convolution Model

• Fuzzy Hammerstein Model

are proposed; these models can more effectively represent special nonlinear dynamic processes than can conventional fuzzy systems.