Fuzzy Feedback Linearizing Controller and Its Equivalence With the Fuzzy Nonlinear Internal Model Control Structure

Abstract

This paper examines the inverse control problem of nonlinear systems with stable dynamics using a fuzzy modeling approach. Indeed, based on the ability of fuzzy systems to approximate any nonlinear mapping, the nonlinear system is represented by a Takagi-Sugeno (TS) fuzzy system, which is then inverted for designing a fuzzy controller. As an application of the proposed inverse control methodology, two popular control structures, namely, feedback linearization and Nonlinear Internal Model Control (NIMC) are investigated. Moreover, the paper points out that, under some conditions, both of the control structures are equivalent and naturally implement a Smith predictor in the presence of time delays.

Exact analytical inversion of interval type-2 TSK fuzzy logic systems with closed form inference methods

“…Therefore, it is easy to obtain the inversion of a fuzzy model. Due to these important properties, the inversion of a fuzzy model is used effectively in model based engineering applications [11][12][13][14][15][16], especially in fuzzy internal model control structures [17][18][19][20][21].…”

Feedback Linearization with Fuzzy Compensation for Uncertain Nonlinear Systems

“…The most common strategies are based on artificial neural networks [2,4,9,10,13] or fuzzy logic [1,3,5,6]. A drawback of these approaches is that both neural networks or fuzzy logic are used to model the entire plant, which means that a large computational effort is normally required to characterize system dynamics.…”

Robust adaptive fuzzy filters output feedback control of strict-feedback nonlinear systems

“…A fuzzy logic system is a universal approximator which, with the increased size of fuzzy rules, can approximate any nonlinearities with arbitrary precision (Wang, 1994). Based on this capability, fuzzy logic systems are vastly adopted for nonlinear systems identification and control (Chen et al, 1996;Denai et al, 2002;Boukezzoula et al, 2007;Qi et al, 2009). Most of them use fuzzy logic systems as nonlinear models for the underlying nonlinearity.…”