Parameter identification of fractional order system using enhanced response sensitivity approach

Liu, Guang; Wang, Li; Luo, Weili; Liu, Jike

doi:10.1016/j.cnsns.2018.07.026

Cited by 31 publications

(4 citation statements)

References 21 publications

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“…Step2: Data preprocessing. Construct the data matrix in accordance with (9) and detect the outliers using (10). Step3: Obtain the frequency domain data.…”

Section: Fdsim For Fossmentioning

confidence: 99%

“…Thus, the system identification technique for a fractional order system (FOS) has attracted the attention of many researchers and engineers. For example, system identification [7], system modeling [8] and parameters identification [9,10]. As a non-iterative scheme working best for the identification of multiple-input multiple-output (MIMO) systems, the subspace identification method has received much attention from the control community, and it has been developed both in the time domain and in the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

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Frequency domain subspace identification of fractional order systems using time domain data with outliers

Wei

Wang

et al. 2020

Asian Journal of Control

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This paper focuses on the identification of the multiple-input multiple-output commensurate fractional order systems. Different from the assumption of known frequency domain data and prior information about noise in the general frequency domain identification algorithm, the reliable frequency domain data is measured in this paper by designing special excitation signals. Then, in order to suppress the impact of noise, the data used in algorithm are truncated from the low frequency band. In addition, this paper considers the case where the sampling data are disturbed by outliers, and a matrix decomposition method with threshold value is developed to eliminate the influence of outliers. After that, the parameters of the system to be identified are estimated by the frequency domain subspace identification method. The validity of the proposed method is demonstrated by an illustrative numerical example.

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“…Step2: Data preprocessing. Construct the data matrix in accordance with (9) and detect the outliers using (10). Step3: Obtain the frequency domain data.…”

Section: Fdsim For Fossmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Frequency domain subspace identification of fractional order systems using time domain data with outliers

Wei

Wang

et al. 2020

Asian Journal of Control

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“…At present, some identification methods of fractional order systems have been studied. e enhanced response sensitivity approach can reduce the sensitivity of the identification parameter results in measurement noise [27]. Block pulse functions can identify fractional order systems with initial conditions [28].…”

Section: Introductionmentioning

confidence: 99%

Recursive Identification for Fractional Order Hammerstein Model Based on ADELS

Jin

Cai

et al. 2021

Mathematical Problems in Engineering

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This paper deals with the identification of the fractional order Hammerstein model by using proposed adaptive differential evolution with the Local search strategy (ADELS) algorithm with the steepest descent method and the overparameterization based auxiliary model recursive least squares (OAMRLS) algorithm. The parameters of the static nonlinear block and the dynamic linear block of the model are all unknown, including the fractional order. The initial value of the parameter is obtained by the proposed ADELS algorithm. The main innovation of ADELS is to adaptively generate the next generation based on the fitness function value within the population through scoring rules and introduce Chebyshev mapping into the newly generated population for local search. Based on the steepest descent method, the fractional order identification using initial values is derived. The remaining parameters are derived through the OAMRLS algorithm. With the initial value obtained by ADELS, the identification result of the algorithm is more accurate. The simulation results illustrate the significance of the proposed algorithm.

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“…In the work of Wang et al, the fractional‐order fuzzy generalized predictive control was discussed based on G‐L difference mentioned in the work of Zhang et al for fractional‐order nonlinear systems. Meanwhile, the problem on unknown parameter in fractional‐order systems was discussed by using the enhanced response sensitivity approach in the work of Liu et al In addition, the fractional‐order average derivative and the Tustin generating function proposed by Chen and Moore were studied in the work of Gao, and two different fractional‐order Kalman filters were presented to achieve more accurate state estimation for fractional‐order linear continuous‐time systems.…”

Section: Introductionmentioning

confidence: 99%

Extended Kalman filters for fractional‐order nonlinear continuous‐time systems containing unknown parameters with correlated colored noises

Huang

Gao

et al. 2019

Intl J Robust & Nonlinear

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Summary By using the Grünwald‐Letnikov (G‐L) difference method and the Tustin generating function method, this study presents extended Kalman filters to achieve satisfactory state estimation for fractional‐order nonlinear continuous‐time systems that containing some unknown parameters with the correlated fractional‐order colored noises. Based on the G‐L difference method and the Tustin generating function method, the difference equations corresponding to fractional‐order nonlinear continuous‐time systems are constructed respectively. The first‐order Taylor expansion is used to linearize the nonlinear functions in the estimated system, which provides the system model for extended Kalman filters. Using the augmented vector method, the unknown parameters are regarded as new state vectors, and the augmented difference equation is constructed. Based on the augmented difference equation, extended Kalman filters are designed to estimate the state of fractional‐order nonlinear systems with process noise as fractional‐order colored noise or measurement noise as fractional‐order colored noise. Meanwhile, the extended Kalman filters proposed in this paper can also estimate the unknown parameters effectively. Finally, the effectiveness of the proposed extended Kalman filters is validated in simulation with two examples.

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Parameter identification of fractional order system using enhanced response sensitivity approach

Cited by 31 publications

References 21 publications

Frequency domain subspace identification of fractional order systems using time domain data with outliers

Frequency domain subspace identification of fractional order systems using time domain data with outliers

Recursive Identification for Fractional Order Hammerstein Model Based on ADELS

Extended Kalman filters for fractional‐order nonlinear continuous‐time systems containing unknown parameters with correlated colored noises

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