Fractional system identification based on improved NLJ algorithm

Zhang, Ming; Li, Dazi

doi:10.1109/ccdc.2012.6244167

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…Peng et al (2010) studied parameter estimation of a class of fractional order time delay systems and developed an FOS model by combining the Harmony optimization algorithm and a linear least squares method. Others extended the new Luus-Jaakola algorithm (Zhang and Li, 2012), a genetic algorithm (Zhou et al, 2013), a modified quantum bacterial foraging algorithm (Liu et al, 2018) and other intelligent optimization algorithms for FOS modelling. In addition, the state-space decomposition method (Wang, 2004) and instrumental variable method (Ste´phane et al, 2013) were also successfully applied for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Fractional order system modelling with Legendre wavelet multi-resolution analysis

Wang

Liang

et al. 2021

Transactions of the Institute of Measurement and Control

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This paper proposes a method of fractional order system (FOS) modelling with Legendre wavelet multi-resolution analysis. The proposed method expands the input and output signals of the system in the form of a Legendre wavelet, and constructs the Legendre wavelet integration operational matrix by use of a block-pulse function. To address the problem of the considerable volume of system identification data and system noise in practical engineering applications, the multi-resolution characteristics of the wavelet are combined to build a wavelet integration operational matrix from the multi-scale space. By continuously discarding the high-frequency information to reduce the length of the identification data, the identification speed of the system is accelerated and the influence of noise on the identification accuracy is reduced. In addition, the least squares method is used to find the optimal order in the identification interval and further accelerate the FOS modelling process. The proposed method rapidly identifies the FOS parameters with high accuracy, and is thus feasible for engineering applications. Its effectiveness is verified by simulation and photoelectric stabilized sighting platform experiment.

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Section: Introductionmentioning

confidence: 99%

Fractional order system modelling with Legendre wavelet multi-resolution analysis

Wang

Liang

et al. 2021

Transactions of the Institute of Measurement and Control

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“…In the literature, it has been involved in a lot of work related to modelling in recent years [1]- [10]. Mete et al [5] presented system modelling based on cascade of a nonlinear second order volterra (SOV) model and a linear FIR model using DE algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…In the other study, the model parameters of time delay systems are estimated using the ABC algorithm [9]. On the other hand, new Luus-Jaakol (NLJ) algorithm is used for the identification of non-integer systems by Ming and Dazi [10].…”

Section: Introductionmentioning

confidence: 99%

ABC Algorithm Based System Modelling for Tuning the Fractional Order PID Controllers

Senberber¹,

Bağış²

2018

ElAEE

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The effectiveness of the model structure for designing control system is highly depending on the right selection of tuning parameters belonging of the algorithms. No doubt, preferable parameter estimation leads to better results for the modelling process. This study consists of two main parts. In the first part, fractional and integer model structures with time delay are proposed for integer-order systems using artificial bee colony (ABC) algorithm and differential evolution (DE) algorithm. The second part of the study is the fractional order PID controller design based on the use of new models obtained with the help of Matlab/Simulink software package. While the integrated square error (ISE) is preferred in the modelling process as the performance criterion, four different performance indices are chosen as ISE, integral time-square error (ITSE), integral absolute error (IAE) and integral timeweighted absolute error (ITAE) during the controller design phase. It is shown that, the results obtained with fractional modelling have achieved better results than the integer order modelling. Furthermore, the controller designs for the algorithm based models proposed in the first stage of the study present a satisfactory performance.

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“…Using fractional state-space models, [11] proposed subspace identification methods which were applicable to both SISO and MIMO fractional order systems. An improved Luus-Jaakola algorithm has been proposed in [12] basing on the discretization of fractional order systems. Liu et al [13] extended the method of modulating functions to the estimation of fractional order systems.…”

Section: Introductionmentioning

confidence: 99%

Memory identification of fractional order systems: Background and theory

Zhao

2015

The 27th Chinese Control and Decision Conference (2015 CCDC)

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This paper presents a novel work that how to determine the memory (initialization function) of fractional order systems by using the recent sampled input-output data. The background and basic theories of initialized fractional order systems are introduced. A practical algorithm is proposed to estimate the initial value of initialization function, which is adaptive to all system parameters. A P-type learning law is applied so that the initialization function can be computed accordingly. The whole process is optimized by using finite system information. The above strategy is available for both Caputo and Riemann-Liouville fractional order systems, where the initial values are applied instead of the initial conditions.

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Fractional system identification based on improved NLJ algorithm

Cited by 4 publications

References 8 publications

Fractional order system modelling with Legendre wavelet multi-resolution analysis

Fractional order system modelling with Legendre wavelet multi-resolution analysis

ABC Algorithm Based System Modelling for Tuning the Fractional Order PID Controllers

Memory identification of fractional order systems: Background and theory

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