Reduced Order Approximation of MIMO Fractional Order Systems

Khanra, Munmun; Pal, Jayanta; Biswas, Karabi

doi:10.1109/jetcas.2013.2265811

Cited by 22 publications

(11 citation statements)

References 18 publications

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“…The T–S fuzzy model (7) is the control objective of FNN decoupling control, which should be expressed as the open loop transfer function model (8) here (Khanra et al, 2013)for i,j=1,2,3,4, where Gij(s)=gij(s)e−τijs and τij>0.…”

Section: Fuzzy Decoupling Predictive Functional Controller Designmentioning

confidence: 99%

Fuzzy decoupling predictive functional control for nonlinear hydro-turbine governing systems with mechanical time delay of the hydraulic servo system

Zhang

Wang

et al. 2022

Journal of Vibration and Control

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This study presents fuzzy decoupling predictive functional control for nonlinear hydro-turbine governing systems with time delay and strong coupling. Here, the Takagi–Sugeno fuzzy approach and fuzzy neural network decoupling algorithm are implemented in the pretreatment of a four-dimensional time delay hydro-turbine governing system model, aiming to solve the nonlinearity and separate coupling variables of the hydro-turbine governing system effectively. Then, a new fuzzy decoupling predictive functional control strategy proposed by combining the simplified hydro-turbine governing system model and predictive function control as well as the robustness and stability of the designed controller are verified by theoretical derivation. Numerical experiment demonstrates effectiveness and superiority of the proposed approach in comparison with fuzzy control under different operation conditions.

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Section: Fuzzy Decoupling Predictive Functional Controller Designmentioning

confidence: 99%

Fuzzy decoupling predictive functional control for nonlinear hydro-turbine governing systems with mechanical time delay of the hydraulic servo system

Zhang

Wang

et al. 2022

Journal of Vibration and Control

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“…Another state-space based approach was detailed using Guidorzi canonical form in [68] based on OE technique. A two-stage technique, for reduced order approximation based on dominant pole, was derived in [69]. Fractional-order TITO processes were discussed with three different decoupling methods in [70].…”

Section: Mimo Fractional-order Systemsmentioning

confidence: 99%

Fractional-Order System Modeling and its Applications

Kothari¹,

Mehta²,

Prasad³

2019

JESTR

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In order to control or operate any system in a closed-loop, it is important to know its behavior in the form of mathematical models. In the last two decades, a fractional-order model has received more attention in system identification instead of classical integer-order model transfer function. Literature shows recently that some techniques on fractional calculus and fractional-order models have been presenting valuable contributions to real-world processes and achieved better results. Such new developments have impelled research into extensions of the classical identification techniques to advanced fields of science and engineering. This article surveys the recent methods in the field and other related challenges to implement the fractional-order derivatives and miss-matching with conventional science. The comprehensive discussion on available literature would help the readers to grasp the concept of fractional-order modeling and can facilitate future investigations. One can anticipate manifesting recent advances in fractional-order modeling in this paper and unlocking more opportunities for research.

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“…Some scholars pay attention to the fractional-order Hammerstein model with single-input single-output [24][25][26][27][28]. Some pay attention to the fractional-order Hammerstein model with multiple-input multiple-output, but most use the state space equation as the linear block of the model [29,30]. However, fractional-order calculus is a whole concept [31].…”

Section: Introductionmentioning

confidence: 99%

Recursive Identification for MIMO Fractional-Order Hammerstein Model Based on AIAGS

Jin

Wang

2022

Mathematics

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In this paper, adaptive immune algorithm based on a global search strategy (AIAGS) and auxiliary model recursive least square method (AMRLS) are used to identify the multiple-input multiple-output fractional-order Hammerstein model. The model’s nonlinear parameters, linear parameters, and fractional order are unknown. The identification step is to use AIAGS to find the initial values of model coefficients and order at first, then bring the initial values into AMRLS to identify the coefficients and order of the model in turn. The expression of the linear block is the transfer function of the differential equation. By changing the stimulation function of the original algorithm, adopting the global search strategy before the local search strategy in the mutation operation, and adopting the parallel mechanism, AIAGS further strengthens the original algorithm’s optimization ability. The experimental results show that the proposed method is effective.

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Reduced Order Approximation of MIMO Fractional Order Systems

Cited by 22 publications

References 18 publications

Fuzzy decoupling predictive functional control for nonlinear hydro-turbine governing systems with mechanical time delay of the hydraulic servo system

Fuzzy decoupling predictive functional control for nonlinear hydro-turbine governing systems with mechanical time delay of the hydraulic servo system

Fractional-Order System Modeling and its Applications

Recursive Identification for MIMO Fractional-Order Hammerstein Model Based on AIAGS

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