Modeling and parametric identification of Hammerstein systems with time delay and asymmetric dead-zones using fractional differential equations

Prasad, Vineet; Mehta, Utkal

doi:10.1016/j.ymssp.2021.108568

Cited by 25 publications

(6 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although simple, the Hammerstein model can capture the nonlinear behavior of many practical processes [7][8][9]. Much effort has been made in estimating the Hammerstein model with approximating static nonlinear block by polynomial functions [10,11], cubic spline functions [12,13], and piecewise linear functions [14,15]. It should be note that these methods can be regard as linear combination of basic functions, for multivariable nonlinear systems, a large number of parameters and high order are required in case the nonlinear block is modeled by using the above-mentioned methods [16].…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Identification Methodology for MIMO Hammerstein Nonlinear Model with Process Noise

Li,

Sun,

Liu

2023

Preprint

View full text Add to dashboard Cite

In this paper, we present a methodology for identifying the multi-input multi-output (MIMO) Hammerstein nonlinear model under colored noise. The Hammerstein model presented is comprised of neural fuzzy models (NFM) as its static nonlinear block and rational transfer functions (RTF) model as its dynamic linear block. The hybrid signals consisting of separable signals and random signals are utilized to deal with the MIMO Hammerstein model identification issue, and the separable signals to implement separation identification of MIMO Hammerstein model is introduced, i.e., the two blocks are separately identified. First, parameters of the linear block are estimated applying correlation function based least squares method in the presence of measurable input-output of Gaussian signals, which can efficiently weaken the process noise interference. Second, estimate of noise parameters vector is to introduced to solve the unknown noise vector in the information matrix, then a recursive extended least squares method is developed for identifying parameters of nonlinear block and colored noise model based on available input-output of random signals. The validity and precision of the presented methodology are demonstrated applying a numerical simulation and a practical nonlinear process, and it is known from the research results, that compare with existing identification techniques, the methodology utilized achieved higher identification accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Identification Methodology for MIMO Hammerstein Nonlinear Model with Process Noise

Li,

Sun,

Liu

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…15 In these studies, linear subsystems are mostly of the fractional-order. 16,17 The calculus of fractional-order gained extensive attention in the past decades and has become a powerful tool for the modeling of real dynamical processes. The identification of fractional-order systems has been an active area of research but most studies have focused on the linear case.…”

Section: Introductionmentioning

confidence: 99%

“…Among its application, it can proven to effectively model a wide class of PH neutralization system, 13 heat exchangers 14 and robot control technology 15 . In these studies, linear subsystems are mostly of the fractional‐order 16,17 . The calculus of fractional‐order gained extensive attention in the past decades and has become a powerful tool for the modeling of real dynamical processes.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation of fractional‐order Hammerstein state space system based on the extended Kalman filter

2023

Adaptive Control & Signal

View full text Add to dashboard Cite

Summary This paper addresses the combined estimation issues of the parameters and states for fractional‐order Hammerstein state space systems with colored noises. An extended state estimator is derived by using the parameter estimates to replace the unknown system parameters in Kalman filter. The hierarchical identification principle is introduced to solve the unknown parameters of measurement noises. By introducing the forgetting factor, an extended Kalman filtering‐based hierarchical forgetting factor stochastic gradient algorithm is presented to estimate the unknown states, parameters and fractional‐order. A numerical example is respectively presented to demonstrate the feasibility of the proposed identification algorithm. It can be seen that the estimation errors are relatively small, which reflects the proposed algorithms have good identification effect.

show abstract

“…Hofmann et al [ 16 ] proposed an offline time-delay identification strategy based on falling film evaporator pilot plant experiments and obtained good results in both validation experiments, with and without evaporation. Prasad et al [ 17 ] used fractional-order modeling technique to identify the parameters of Hammerstein structured nonlinear systems with discontinuous asymmetric (two segment piecewise-linear with a dead-zone) nonlinearity and input time delay. Li et al [ 18 ] designed a discrete-time robust adaptive estimator to identify the time delay and sandwich system parameters.…”

Section: Introductionmentioning

confidence: 99%

Multi-Delay Identification of Rare Earth Extraction Process Based on Improved Time-Correlation Analysis

Liu

Yang

et al. 2023

Sensors

View full text Add to dashboard Cite

The rare earth extraction process has significant time delay characteristics, making it challenging to identify the time delay and establish an accurate mathematical model. This paper proposes a multi-delay identification method based on improved time-correlation analysis. Firstly, the data are preprocessed by grey relational analysis, and the time delay sequence and time-correlation data matrix are constructed. The time-correlation analysis matrix is defined, and the H∞ norm quantifies the correlation degree of the data sequence. Thus the multi-delay identification problem is transformed into an integer optimization problem. Secondly, an improved discrete state transition algorithm is used for optimization to obtain multi-delay. Finally, based on an Neodymium (Nd) component content model constructed by a wavelet neural network, the performance of the proposed method is compared with the unimproved time delay identification method and the model without an identification method. The results show that the proposed algorithm improves optimization accuracy, convergence speed, and stability. The performance of the component content model after time delay identification is significantly improved using the proposed method, which verifies its effectiveness in the time delay identification of the rare earth extraction process.

show abstract

Modeling and parametric identification of Hammerstein systems with time delay and asymmetric dead-zones using fractional differential equations

Cited by 25 publications

References 32 publications

WITHDRAWN: Identification Methodology for MIMO Hammerstein Nonlinear Model with Process Noise

WITHDRAWN: Identification Methodology for MIMO Hammerstein Nonlinear Model with Process Noise

Parameter estimation of fractional‐order Hammerstein state space system based on the extended Kalman filter

Multi-Delay Identification of Rare Earth Extraction Process Based on Improved Time-Correlation Analysis

Contact Info

Product

Resources

About