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The paper presents a comparative analysis of extended horizon model predictive control deployed for two various state prediction algorithms designed for discrete-time fractional-order state space systems. The first one is a finite fractional difference state predictor derived on the basis of a finite-length approximation of the fractional-order state space system. The second one is a BTA-based integer-order...
This paper presents new results in simulation analysis of Al-Alaloui-based discretization scheme for fractional-order derivative. The analysis is performed both in frequency and time domains. A series of simulation analyses provide to formulation of some implementation highlights related to approximation of fractional-order derivative with the Al-Alaloui operator.
In this paper, a Grünwald-Letnikov-originated fractional-order difference (FD) is modeled with finite-length approximators called a finite fractional difference (FFD) and a normalized finite fractional difference (NFFD), whose time-domain structure enables their effective employment in the prediction process for linear discrete-time fractional-order state-space systems. The main, original contribution...
This paper presents a general, modified framework for various time-domain approximations to the Grünwald-Letnikov fractional difference, namely finite fractional and Laguerre-based differences. The approximations are applied in the modeling problem for linear fractional-order state space systems, with two different implementation schemes presented.
This paper presents a comparative analysis of two methods which can be used to modeling a nonlinear characteristic of the Hammerstein system. The first one is based on the polynomial expansion and the second is based on the Radial Basis Functions.
This paper presents a new implementable strategy for modeling and identification of a fractional-order discrete-time nonlinear block-oriented SISO Wiener system. The concept of modeling of a linear dynamics by means of orthonormal basis functions (OBF) is employed to separate linear and nonlinear submodels, which enables a linear regression formulation of the parameter estimation problem. Finally,...
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