Abstract

The main aim of this paper is to present and compare three approaches to uncertainty modeling and robust stability analysis for fractional-order (FO) linear time-invariant (LTI) single-input single-output (SISO) uncertain systems. The investigated objects are described either via FO models with parametric uncertainty, by means of FO unstructured multiplicative uncertainty models, or through FO unstructured additive uncertainty models, while the unstructured models are constructed on the basis of appropriate selection of a nominal plant and a weight function. Robust stability investigation for systems with parametric uncertainty uses the combination of plotting the value sets and application of the zero exclusion condition. For the case of systems with unstructured uncertainty, the graphical interpretation of the utilized robust stability test is based mainly on the envelopes of the Nyquist diagrams. The theoretical foundations are followed by two extensive, illustrative examples where the plant models are created; the robust stability of feedback control loops is analyzed, and obtained results are discussed.

Highlights

  • The impact of FO calculus ([1,2,3,4,5]) on real-life applications has been rapidly growing lately

  • The main aim of this paper is to present and compare three approaches to uncertainty modeling and robust stability analysis for fractional-order (FO) linear time-invariant (LTI) single-input single-output (SISO) uncertain systems

  • The obtained outcomes indicate that one should be aware of potential conservatism in the investigation of robust stability when the (FO) system with parametric uncertainty is modelled as a (FO) system with unstructured uncertainty

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Summary

Introduction

The impact of FO calculus ([1,2,3,4,5]) on real-life applications has been rapidly growing lately. It has already significantly influenced areas such as robotics [6, 7], signal processing [8], electrical circuits [9, 10] and fractance devices [11], bioengineering [12], viscoelasticity [13], and chaos theory [14]. Incorporating the uncertainty into the multipleinput multiple-output (MIMO) models is based mainly on so-called structured uncertainty and linear fractional transformations [35]

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