Parameter learning for the nonlinear system described by Hammerstein model with output disturbance

Li, Feng; Zhu, Xun; He, Naibao; Gu, Yong

doi:10.1002/asjc.2829

Cited by 23 publications

(8 citation statements)

References 37 publications

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“…The Hammerstein model, one of the most frequently used block-oriented models, is composed by the cascading of a static nonlinearity and a linear block. 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].…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Identification Methodology for MIMO Hammerstein Nonlinear Model with Process Noise

Li,

Sun,

Liu

2023

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Identification Methodology for MIMO Hammerstein Nonlinear Model with Process Noise

Li,

Sun,

Liu

2023

Preprint

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“…The variables and corresponding operation condition in Van de Vusse reaction are set in Table 4 41 . For the continuous stirred tank reactor (CSTR) process, its purpose is to adjust the concentration C B by means of regulating the flow rate F .…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Estimation of wiener nonlinear systems with measurement noises utilizing correlation analysis and Kalman filter

Li,

Qian,

et al. 2024

Intl J Robust & Nonlinear

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This paper is concerned with parameter estimation of Wiener systems with measurement noises employing correlation analysis method and adaptive Kalman filter. The presented Wiener system consists of two series blocks, that is, a dynamic block represented by auto‐regressive moving average (ARMA) model, and static nonlinear block established by neural fuzzy model. Aim at estimating separately the two blocks, the separable signals are introduced. First, applying the separable signals to decouple the identification of linear dynamic block from that of static nonlinear block, then ARMA model parameters are estimated employing correlation function‐based least squares principle. Moreover, aiming at handle with error caused by colored measurement noise, adaptive Kalman filter technique and cluster method are introduced to estimate parameter of the nonlinear block and noises model, enhancing parameter estimation precision. The accuracy and applicability of estimated scheme presented are verified through numerical simulation and nonlinear process, the results demonstrate that it is feasible for estimating the Wiener systems in the presence of colored measurement noises.

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“…In most of the relevant literatures (like Cui et al, 2017a, 2017b), a comparative study was made between proposed improved cubature Kalman filter (ICKF) or augmented form of ICKF with traditional CKF approach, where respective authors have focused on performance indices between the two estimation schemes (Lie et al, 2021, 2022). Motivated by the previous relevant works, we have analysed performance indices like RMSE, TV or converging performance (MSD) between our proposed control strategy and traditional ACKF-FSMC scheme.…”

Section: Resultsmentioning

confidence: 99%

Disturbance-observer-based finite time sliding mode controller with unmatched uncertainties utilizing improved cubature Kalman filter

Sindhuja

Panda

Velappan

et al. 2023

Transactions of the Institute of Measurement and Control

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Complex reactions are difficult to control as the reactants not only produce desired product but may also further undergo unwanted reactions or the original reactants may degrade through alternate paths. This can be improved by selecting proper measurement and control. In this paper, a robust augmented derivative-free Kalman filter (augmented cubature Kalman filter, ACKF) is propounded for the continuous stirred tank reactor (CSTR) carrying out Van-de-Vusse type complex reactions where the intermediate is the desired product in the midst of consecutive and parallel reactions. Non-isothermal CSTR shows strong non-linearity with inherent challenges as the reaction kinetics depends on process temperature through Arrhenius laws and accompanies non-ideal flow behaviour and unmeasurable disturbances. First, the process exhibits inverse or non-minimum phase behaviour. Second, to control product temperature and concentration, a finite time sliding mode controller (FSMC) is suggested which effectively tracks servo response to the desired set point. Third, a disturbance filter has been proposed in association with FSMC, which constructively eliminates the input disturbances. Realistic simulations for servo-regulatory compliance, elimination of measurement noise, impact on large perturbation, and parametric uncertainty with a state-of-the-art simulator ensure the efficacy of the proposed controller. The results are analysed further with a derivative-free FSMC control approach, considering various performance indices (root mean square error (RMSE), total variation (TV), mean square deviation (MSD)) to assess the goodness of the newly developed control strategy. It has been found that improved control law (improved augmented cubature Kalman filter (IACKF)-FSMC) yields better RMSE (7.2645e–05) than ACKF-FSMC (7.5814e–05).

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Parameter learning for the nonlinear system described by Hammerstein model with output disturbance

Cited by 23 publications

References 37 publications

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

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

Estimation of wiener nonlinear systems with measurement noises utilizing correlation analysis and Kalman filter

Disturbance-observer-based finite time sliding mode controller with unmatched uncertainties utilizing improved cubature Kalman filter

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