Takagi–Sugeno model synthesis of a quasi-linear multiwheeled mobile robot

Abstract

This paper presents the application of the Takagi–Sugeno (TS) model synthesis for a quasi-linear time invariant (QLTI) four wheel differentially steered mobile robot. The focus is the mathematical description of the mobile robot model from physical properties as a QLTI and the application of two TS-based fuzzy logic models by means of two modified subtractive clustering approaches. Using the QLTI model data and the available mobile robot experimental data together with two modified versions of the subtractive clustering method, several local linear models were identified using first-order multivariate TS models within specified normalized error bounds. The identified TS local models resulted in a family of LTI models over subsets of the universe of discourse, which are equivalent to the linearized QLTI models. The TS-blended model synthesis that uses a linguistic description in the TS antecedent and the explicit mathematical mobile robot description in the consequent resulted in a close approximation to the quasi-linear model dynamics. The resulted TS mobile robot modelling approach effectiveness is demonstrated by means of the analytical mobile robot description (QLTI) and experimental (raw) data.