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Journal articles on the topic 'Hammerstein model'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Liutkevičius, R. "Fuzzy Hammerstein Model of Nonlinear Plant." Nonlinear Analysis: Modelling and Control 13, no. 2 (2008): 201–12. http://dx.doi.org/10.15388/na.2008.13.2.14580.

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This paper presents the synthesis and analysis of the enhanced predictive fuzzy Hammerstein model of the water tank system. Fuzzy Hammerstein model was compared with three other fuzzy models: the first was synthesized using Mamdani type rule base, the second – Takagi-Sugeno type rule base and the third – composed of Mamdani and Takagi-Sugeno rule bases. The synthesized model is invertible so it can be used in the model based control. The fuzzy Hammerstein model was synthesized to eliminate disadvantages of the other fuzzy models. The advantage of the fuzzy Hammerstein model was experimentally

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2

Mohd, Ashraf Ahmad, Musa Zulkifli, Helmi Suid Mohd, and Zaidi Mohd Tumari Mohd. "Grey wolf optimizer for identification of liquid slosh behavior using continuous-time Hammerstein model." Bulletin of Electrical Engineering and Informatics 9, no. 2 (2020): 542–49. https://doi.org/10.11591/eei.v9i2.2074.

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This paper presents the identification of liquid slosh plant using the Hammerstein model based on Grey Wolf Optimizer (GWO) method. A remote car that carrying a container of liquid is considered as the liquid slosh experimental rig. In contrast to other research works, this paper consider a piece-wise affine function in the nonlinear function of the Hammerstein model, which is more generalized function. Moreover, a continuous-time transfer function is utilized in the Hammerstein model, which is more suitable to represent a real system. The GWO method is used to tune both coefficients in the no

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3

Jinxing, Shen, Cui Hongxin, Feng Ke, Zhang Hong, and Li Huanliang. "Parameter Identification and Control Algorithm of Electrohydraulic Servo System for Robotic Excavator Based on Improved Hammerstein Model." Mathematical Problems in Engineering 2020 (May 23, 2020): 1–9. http://dx.doi.org/10.1155/2020/9216019.

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In view of the nonlinearity and time-varying characteristics of the electrohydraulic servo system of the robotic excavator, a nonlinear adaptive identification and control algorithm based on improved Hammerstein model is proposed. The Hammerstein algorithm model can approximate the nonlinear system with enough precision, but for the time-varying systems is not satisfactory. In order to compensate for the influence of time-varying factors, the fuzzy control module is designed to adaptively update the forgetting factor. The experimental results show that the improved Hammerstein model error is a

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4

Fruzzetti, K. P., A. Palazoğlu, and K. A. McDonald. "Nolinear model predictive control using Hammerstein models." Journal of Process Control 7, no. 1 (1997): 31–41. http://dx.doi.org/10.1016/s0959-1524(97)80001-b.

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5

Teng, Qin, Jian Bo Zhang, and Peng An. "Dynamic Modeling for a Non-Linear System Based on the MOESP Algorithm." Applied Mechanics and Materials 164 (April 2012): 467–72. http://dx.doi.org/10.4028/www.scientific.net/amm.164.467.

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In order to measure accurately air fuel ratio in the engine exhaust gas, the Hammerstein model of the exhaust gas oxygen (EGO) sensor was identified using the multivariable output error state space (MOESP) algorithm. Firstly, a static model of the EGO sensor was identified based on engine experiment data as a static nonlinear part of the Hammerstein model. Then, the MOESP algorithm was used to build a state space model (SSM) of dynamic linear part of the Hammerstein model. To estimate model order, the Akaike Information Criterion (AIC) and the Minimum Description Length (MDL) criterion were co

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Porkuian, Olga, Vladimir Morkun, Natalia Morkun, and Oleksandra Serdyuk. "Predictive Control of the Iron Ore Beneficiation Process Based on the Hammerstein Hybrid Model." Acta Mechanica et Automatica 13, no. 4 (2019): 262–70. http://dx.doi.org/10.2478/ama-2019-0036.

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Abstract Non-linear, dynamic, non-stationary properties characterize objects of the iron ore beneficiation line. Therefore, for their approximation, it is advisable to use models of the Hammerstein class. As a result of comparing the three models of Hammerstein: simple, parallel and recursive-parallel, it was shown that the best result for identifying the considered processes of magnetic beneficiation of iron ore by the minimum error criterion was obtained using the Hammerstein recursive-parallel model. Hence, it is recommended for the identification of beneficiation production objects.

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Qin, Ke, and Jing Chen. "Parameters Identification of Dual-Rate Hammerstein Systems Based on Finite Input Response Models." Applied Mechanics and Materials 204-208 (October 2012): 4742–45. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.4742.

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This paper studies the parameter estimation for dual-rate Hammerstein systems. The finite input response model is used to approximate the dual-rate Hammerstein systems, and a recursive least squares algorithm is derived to estimate the parameters of the finite input response model. Based on the estimated parameters, the origin parameters of the dual-rate Hammerstein systems can be obtained. An example is provided to show the effectiveness of the proposed method.

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8

Huang, Ai Qin, and Yong Wang. "The Hammerstein Predict Model of Control Valve Based on Least Square Support Vector Machine." Applied Mechanics and Materials 472 (January 2014): 164–70. http://dx.doi.org/10.4028/www.scientific.net/amm.472.164.

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Control valve is a kind of essential terminal control component controlling the parameters of fluid such as flow and pressure in process-control. However it is a complex nonlinear, multi-input and single-output (MISO) system that is hard to model by traditional methodologies. To establish the pressure model of control valve, this paper presents a Hammerstein modeling method based on the least squares support vector machines (LS-SVM). The linear model parameters and the static nonlinearity of Hammerstein model can be obtained simultaneously by solving a set of linear equations followed by the s

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9

Zhang, Shuo, Dongqing Wang, and Yaru Yan. "Instrumental Variable-Based OMP Identification Algorithm for Hammerstein Systems." Complexity 2018 (July 22, 2018): 1–10. http://dx.doi.org/10.1155/2018/8420426.

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Hammerstein systems are formed by a static nonlinear block followed by a dynamic linear block. To solve the parameterizing difficulty caused by parameter coupling between the nonlinear part and the linear part in a Hammerstein system, an instrumental variable method is studied to parameterize the Hammerstein system. To achieve in simultaneously identifying parameters and orders of the Hammerstein system and to promote the computational efficiency of the identification algorithm, a sparsity-seeking orthogonal matching pursuit (OMP) optimization method of compressive sensing is extended to ident

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10

Yu, Hongshan, Jinzhu Peng, and Yandong Tang. "Identification of Nonlinear Dynamic Systems Using Hammerstein-Type Neural Network." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/959507.

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Hammerstein model has been popularly applied to identify the nonlinear systems. In this paper, a Hammerstein-type neural network (HTNN) is derived to formulate the well-known Hammerstein model. The HTNN consists of a nonlinear static gain in cascade with a linear dynamic part. First, the Lipschitz criterion for order determination is derived. Second, the backpropagation algorithm for updating the network weights is presented, and the stability analysis is also drawn. Finally, simulation results show that HTNN identification approach demonstrated identification performances.

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11

Song, E. Nuo, Na Li, and Guo Xin Wang. "Constrained Model Algorithmic Control for the Generalized Hammerstein Model with Impulse Response." Advanced Materials Research 211-212 (February 2011): 914–17. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.914.

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A Generalized Hammerstein Model with Impulse Response for symmetric nonlinear systems was presented in this paper. A hyper-quadratic object function was developed by adding highest order control input term with a symbolic function into the object function, and a constrained multi-step model algorithmic control for the non-minimum phase systems with open-loop stable characterization was established by forcing the control input with saturated limitation. The algorithm with one control policy can guarantee the simulative results without steady state deviation and the control input being converged

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12

Klein, A., T. Konrad, and D. Abel. "Data-driven state estimation toward blade individual load-reduction of wind turbines¹." Journal of Physics: Conference Series 2265, no. 3 (2022): 032073. http://dx.doi.org/10.1088/1742-6596/2265/3/032073.

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Abstract As wind turbines tend to be build larger every year, the importance of load-reducing controls to damp the rotor blade oscillations increases. For a control algorithm to perform blade individual load-reducing strategies, online information about each rotor blade’s deflection is needed. An approach for wind turbine state estimation with special attention to the rotor blade deflections is given using a Hammerstein model driven by simulation data in a Kalman Filter (KF). The Hammerstein’s linear submodel is mainly derived as time discrete state space system via least squares, while nonlin

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13

Brouri, A., and S. Slassi. "Identification of Nonlinear Systems Structured by Wiener-Hammerstein Model." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (2016): 167. http://dx.doi.org/10.11591/ijece.v6i1.8694.

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Wiener-Hammerstein systems consist of a series connection including a nonlinear static element sandwiched with two linear subsystems. The problem of identifying Wiener-Hammerstein models is addressed in the presence of hard nonlinearity and two linear subsystems of structure entirely unknown (asymptotically stable). Furthermore, the static nonlinearity is not required to be invertible. Given the system nonparametric nature, the identification problem is presently dealt with by developing a two-stage frequency identification method, involving simple inputs.

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Brouri, A., and S. Slassi. "Identification of Nonlinear Systems Structured by Wiener-Hammerstein Model." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (2016): 167. http://dx.doi.org/10.11591/ijece.v6i1.pp167-176.

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Wiener-Hammerstein systems consist of a series connection including a nonlinear static element sandwiched with two linear subsystems. The problem of identifying Wiener-Hammerstein models is addressed in the presence of hard nonlinearity and two linear subsystems of structure entirely unknown (asymptotically stable). Furthermore, the static nonlinearity is not required to be invertible. Given the system nonparametric nature, the identification problem is presently dealt with by developing a two-stage frequency identification method, involving simple inputs.

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15

Sbeity, Fatima, Sébastien Ménigot, Jamal Charara, and Jean-Marc Girault. "Contrast Improvement in Sub- and Ultraharmonic Ultrasound Contrast Imaging by Combining Several Hammerstein Models." International Journal of Biomedical Imaging 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/270523.

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Sub- and ultraharmonic (SUH) ultrasound contrast imaging is an alternative modality to the second harmonic imaging, since, in specific conditions it could produce high quality echographic images. This modality enables the contrast enhancement of echographic images by using SUH present in the contrast agent response but absent from the nonperfused tissue. For a better access to the components generated by the ultrasound contrast agents, nonlinear techniques based on Hammerstein model are preferred. As the major limitation of Hammerstein model is its capacity of modeling harmonic components only

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16

Yao, Jie, Yong Hong Zhu, and Jiang Hong Wang. "The Influence of General Disturbances on the Hammerstein Model Identifications." Advanced Materials Research 588-589 (November 2012): 1194–98. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1194.

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The linear dynamic Hammerstein model presents some static nonlinearity. The predominant method to estimate the components of this model is to minimize the error between the simulated and measured outputs. Taking the Gaussian input signals into account, we can estimate the linear part of the Hammerstein model by using the Buss gang’s classic theory. Accordingly, considering the case in point with the general disturbances, we come up with the maximum likelihood methodology.

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17

Julakha, Jahan Jui, Ashraf Ahmad Mohd, and Ikram Mohd Rashid Muhammad. "Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey." Bulletin of Electrical Engineering and Informatics 11, no. 1 (2022): 454–65. https://doi.org/10.11591/eei.v11i1.3296.

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Metaheuristics have been acknowledged as an effective solution for many difficult issues related to optimization. The metaheuristics, especially swarm’s intelligence and evolutionary computing algorithms, have gained popularity within a short time over the past two decades. Various metaheuristics algorithms are being introduced on an annual basis and applications that are more new are gradually being discovered. This paper presents a survey for the years 2011-2021 on multiple metaheuristics algorithms, particularly swarm and evolutionary algorithms, to identify a nonlinear block-oriented

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18

Rahmani, Mohammad-Reza, and Mohammad Farrokhi. "Nonlinear dynamic system identification using neuro-fractional-order Hammerstein model." Transactions of the Institute of Measurement and Control 40, no. 13 (2017): 3872–83. http://dx.doi.org/10.1177/0142331217734301.

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This paper presents a neuro-fractional-order Hammerstein model with a systematic identification algorithm for identifying unknown nonlinear dynamic systems. The proposed model consists of a Radial Basis Function Neural Network (RBF NN) followed by a Fractional-Order System (FOS). The proposed identification scheme is performed in two stages. First, the fractional-order and the number of state variables (or degree) of the state-space realization of the FOS are estimated in the frequency domain. Then, the parameters of the RBF NN (the weights, centers and widths of the Gaussian functions) and th

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19

Liu, Qing Song, Zhen Zhang, and J. Q. Mao. "Modeling and Control for GMA Based on Hammerstein Model Structure." Advanced Materials Research 668 (March 2013): 406–9. http://dx.doi.org/10.4028/www.scientific.net/amr.668.406.

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A rate-dependent hysteresis model for Giant Magnetostrictive Actuator (GMA) is proposed based on Hammerstein model structure. The Generalized Prandtl-Ishlinskii (GPI) model is used to represent nonlinear block in Hammerstein model. The validity of model is examined by comparsion between simulation results and experimental data. Based on the proposed model, a PID feedback controller combined with an inverse compensation in the feedforward loop is used for tracking control. Experimental results show that the control strategy is effective.

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20

Jui, Julakha Jahan, Mohd Ashraf Ahmad, Mohamed Sultan Mohamed Ali, Mohd Anwar Zawawi, and Mohd Falfazli Mat Jusof. "Identification of the Thermoelectric Cooler Using Hybrid Multi-Verse Optimizer and Sine Cosine Algorithm Based Continuous-Time Hammerstein Model." Cybernetics and Information Technologies 21, no. 3 (2021): 160–74. http://dx.doi.org/10.2478/cait-2021-0036.

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Abstract This paper presents the identification of the ThermoElectric Cooler (TEC) plant using a hybrid method of Multi-Verse Optimizer with Sine Cosine Algorithm (hMVOSCA) based on continuous-time Hammerstein model. These modifications are mainly for escaping from local minima and for making the balance between exploration and exploitation. In the Hammerstein model identification a continuous-time linear system is used and the hMVOSCA based method is used to tune the coefficients of both the Hammerstein model subsystems (linear and nonlinear) such that the error between the estimated output a

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21

Li, Feng, Yinsheng Luo, Naibao He, Ya Gu, and Qingfeng Cao. "Data-driven Learning Algorithm of Neural Fuzzy Based Hammerstein-Wiener System." Journal of Sensors 2021 (July 30, 2021): 1–11. http://dx.doi.org/10.1155/2021/8920329.

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A novel data-driven learning approach of nonlinear system represented by neural fuzzy Hammerstein-Wiener model is presented. The Hammerstein-Wiener system has two static nonlinear blocks represented by two independent neural fuzzy models surrounding a dynamic linear block described by finite impulse response model. The multisignal theory is designed for employing Hammerstein-Wiener system to separate parameter learning issues. To begin with, the output nonlinearity parameters are learned utilizing separable signal with different amplitudes. Furthermore, correlation analysis method is implement

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22

Liu, You-Jiang, Bang-Hua Zhou, Jie Zhou, and Yi-Nong Liu. "A Two-Step Identification Approach for Twin-Box Models of RF Power Amplifier." International Journal of Microwave Science and Technology 2011 (September 18, 2011): 1–5. http://dx.doi.org/10.1155/2011/468497.

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We propose a two-step identification approach for twin-box model (Wiener or Hammerstein) of RF power amplifier. The linear filter block and the static nonlinearity block are extracted, respectively, based on least-squares method, by iterative calculation. Simulations show that the method can get quite accurate parameters to model different nonlinear models with memory such as Wiener, Hammerstein, Wiener-Hammerstein (W-H), and memory polynomial models, hence, demonstrating its robustness. Furthermore, experimental results show excellent agreement between measured output and modeled output, wher

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23

PORKUIAN, Olga, Vladimir MORKUN, Natalia MORKUN, and Irina GAPONENKO. "THE INFLUENCE OF THE CHARACTERISTICS VARIATIONS OF THE CONCENTRATING PLANT CONTROL OBJECT ON THE IDENTIFICATION RESULTS USING THE HAMMERSTEIN MODEL." Sustainable Development of Mountain Territories 13, no. 1 (2021): 94–102. http://dx.doi.org/10.21177/1998-4502-2021-13-1-94-102.

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As a result of the identification based on the Hammerstein model of objects of the first stage of iron ore magnetic separation, the adequacy of the model is obtained. All results of the testing of the developed identification algorithms show that the subsystem of identification of the automated process control systems of processing plants based on the Hammerstein hybrid model allows to carry out satisfactory identification of objects and, as a consequence, to improve the quality of technological processes. The study of the influence of the coefficient of various typical links on the results of

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24

Al-Dhaifallah, Mujahed, Kottakkaran Nisar, Praveen Agarwal, and Alaa Elsayyad. "Modeling and identification of heat exchanger process using least squares support vector machines." Thermal Science 21, no. 6 Part B (2017): 2859–69. http://dx.doi.org/10.2298/tsci151026204a.

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In this paper, Hammerstein model and non-linear autoregressive with eXogeneous inputs (NARX) model are used to represent tubular heat exchanger. Both models have been identified using least squares support vector machines based algorithms. Both algorithms were able to model the heat exchanger system with-out requiring any a priori assumptions regarding its structure. The results indicate that the blackbox NARX model outperforms the NARX Hammerstein model in terms of accuracy and precision.

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25

Lv, Li Xing, and Jing Chen. "Modified Stochastic Gradient Algorithm for Hammerstein Systems." Applied Mechanics and Materials 336-338 (July 2013): 2320–23. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2320.

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This paper proposes a modified stochastic gradient algorithm for Hammerstein systems. By the Weierstrass approximation theorem, the model of the nonlinear Hammerstein systems be changed to an identification model, then based on the derived model, a modified stochastic gradient identification algorithm is used to estimate all the unknown parameters of the systems. An example is provided to show the effectiveness of the proposed algorithm.

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Li, Feng, Lianyu Chen, Songlin Wo, Shengquan Li, and Qingfeng Cao. "Modeling and parameter learning method for the Hammerstein–Wiener model with disturbance." Measurement and Control 53, no. 5-6 (2020): 971–82. http://dx.doi.org/10.1177/0020294020912790.

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In this paper, a novel modeling and parameter learning method for the Hammerstein–Wiener model with disturbance is proposed, and the Hammerstein–Wiener model is implemented to approximate complex nonlinear industrial processes. The proposed Hammerstein–Wiener model has two static nonlinear blocks represented by two independent neuro-fuzzy models that surround a dynamic linear block described by the finite impulse response model. The parameter learning method of the Hammerstein–Wiener model with disturbance can be summarized in the following three steps: First, the designed input signals are im

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27

Feng, Yi, Zhi Yuan Qian, Bao Chun Lu, and Deng Feng Zhang. "Research on Nonlinear Commutating Characteristics of Valve-Controlled Motor System Based on Hammerstein Model." Key Engineering Materials 693 (May 2016): 1772–79. http://dx.doi.org/10.4028/www.scientific.net/kem.693.1772.

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Aiming at the low accuracy problem of linear model of Valve-controlled motor system in conditions of frequent start-stop and commutation, the nonlinear characteristics of the main components of valve-controlled motor system are analyzed, and non-linear mathematical models and simulation models are established to study the impact on the system of each nonlinear characteristic separately. Hammerstein model consists of a static nonlinear part and dynamic linear part connected in series is adopted, and the non-linear characteristics are introduced to the system. The multi-frequency sinusoidal inpu

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28

EMARA-SHABAIK, Hosam E., Mohammed S. AHMED, and Khaled H. AL-AJMI. "Wiener-Hammerstein Model Identification-Recursive Algorithms." JSME International Journal Series C 45, no. 2 (2002): 606–13. http://dx.doi.org/10.1299/jsmec.45.606.

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29

Chaoui, F. Z., F. Giri *, Y. Rochdi, M. Haloua, and A. Naitali. "System identification based on Hammerstein model." International Journal of Control 78, no. 6 (2005): 430–42. http://dx.doi.org/10.1080/00207170500096666.

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30

Rasouli, Mohammad, David Westwick, and William Rosehart. "Quasiconvexity analysis of the Hammerstein model." Automatica 50, no. 1 (2014): 277–81. http://dx.doi.org/10.1016/j.automatica.2013.11.004.

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31

Meher, Trusna, and Somanath Majhi. "Relay based identification of Hammerstein model." International Journal of Dynamics and Control 6, no. 4 (2018): 1599–607. http://dx.doi.org/10.1007/s40435-018-0395-1.

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32

Ozer, Saban, Hasan Zorlu, and Selcuk Mete. "System identification application using Hammerstein model." Sādhanā 41, no. 6 (2016): 597–605. http://dx.doi.org/10.1007/s12046-016-0505-8.

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33

Chiu, Hsin-Lin. "Identification Approach for Nonlinear MIMO Dynamics of Closed-Loop Active Magnetic-Bearing System." Applied Sciences 12, no. 17 (2022): 8556. http://dx.doi.org/10.3390/app12178556.

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A systematic identification approach for the rotor/radial active magnetic bearing (rotor/RAMB) system is presented in this study. First, the system identification of the controller of commercial TMP is undertaken, and the corresponding linear dynamic models are constructed. To perfectly excite the nonlinearities of the rotor/RAMB system, a parallel amplitude-modulated pseudo-random binary sequence (PAPRBS) generator, which possesses the merits of no correlation among the perturbation signals, is employed. The dynamics of the rotor/RAMB system is identified with a Hammerstein–Wiener model. To r

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Hjalmarsson, Håkan, and Jonas Mårtensson. "Finite model order accuracy in Hammerstein model estimation." Automatica 48, no. 10 (2012): 2640–46. http://dx.doi.org/10.1016/j.automatica.2012.06.067.

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35

Mehmood, Khizer, Naveed Ishtiaq Chaudhary, Zeshan Aslam Khan, et al. "Nonlinear Hammerstein System Identification: A Novel Application of Marine Predator Optimization Using the Key Term Separation Technique." Mathematics 10, no. 22 (2022): 4217. http://dx.doi.org/10.3390/math10224217.

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The mathematical modelling and optimization of nonlinear problems arising in diversified engineering applications is an area of great interest. The Hammerstein structure is widely used in the modelling of various nonlinear processes found in a range of applications. This study investigates the parameter optimization of the nonlinear Hammerstein model using the abilities of the marine predator algorithm (MPA) and the key term separation technique. MPA is a population-based metaheuristic inspired by the behavior of predators for catching prey, and utilizes Brownian/Levy movement for predicting t

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Kanthasamy, Ramesh, Hisyam Anwaruddin, and Suriya Kumar Sinnadurai. "A New Approach to the Identification of Distillation Column Based on Hammerstein Model." Modelling and Simulation in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/813757.

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Developing a suitable nonlinear model is the most challenging problem in the application of nonlinear model based controllers to distillation column. Hammerstein model consists of a nonlinear static element described by wavenet based nonlinear function, followed by a linear dynamic element described by the Output Error(OE) model was used in this study to represent the nonlinear dynamics of the distillation column. The model parameters were identified using iterative prediction-error minimization method. The model validation results proved that the Hammerstein model was capable of capturing the

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37

Lopes dos Santos, P., José A. Ramos, and J. L. Martins de Carvalho. "Identification of a Benchmark Wiener–Hammerstein: A bilinear and Hammerstein–Bilinear model approach." Control Engineering Practice 20, no. 11 (2012): 1156–64. http://dx.doi.org/10.1016/j.conengprac.2012.04.002.

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Yan, Jun, Bo Li, Hai-Feng Ling, Hai-Song Chen, and Mei-Jun Zhang. "Nonlinear State Space Modeling and System Identification for Electrohydraulic Control." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/973903.

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The paper deals with nonlinear modeling and identification of an electrohydraulic control system for improving its tracking performance. We build the nonlinear state space model for analyzing the highly nonlinear system and then develop a Hammerstein-Wiener (H-W) model which consists of a static input nonlinear block with two-segment polynomial nonlinearities, a linear time-invariant dynamic block, and a static output nonlinear block with single polynomial nonlinearity to describe it. We simplify the H-W model into a linear-in-parameters structure by using the key term separation principle and

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Umar, Hafiz Muhammad, Ruichen Yu, Zhiyuan Gao, and Hesheng Zhang. "Enhancing model estimation accuracy and convergence rate in hysteresis modeling of MFC actuators using modified differential evolution algorithm." Vibroengineering PROCEDIA 49 (May 18, 2023): 246–52. http://dx.doi.org/10.21595/vp.2023.23309.

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This paper presents a study on improving the estimation accuracy and convergence rate of hysteresis modeling of MFC actuators using mutation enhanced differential evolution (MEDE) algorithm, a modified version of the differential evolution algorithm. The proposed MEDE algorithm uses three mutation strategies, i.e., best, rand, and pbest. To model the secondary path of a smart flexible beam with MFC actuators, a Hammerstein model that combines an asymmetric Bouc-Wen model with an ARX model connected in series is proposed. The fitness function values of the Hammerstein model are compared with ev

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Nikolic, Sasa S., Miroslav B. Milovanovic, Nikola B. Dankovic, et al. "Identification of Nonlinear Systems Using the Hammerstein-Wiener Model with Improved Orthogonal Functions." Elektronika ir Elektrotechnika 29, no. 2 (2023): 4–11. http://dx.doi.org/10.5755/j02.eie.33838.

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Hammerstein-Wiener systems present a structure consisting of three serial cascade blocks. Two are static nonlinearities, which can be described with nonlinear functions. The third block represents a linear dynamic component placed between the first two blocks. Some of the common linear model structures include a rational-type transfer function, orthogonal rational functions (ORF), finite impulse response (FIR), autoregressive with extra input (ARX), autoregressive moving average with exogenous inputs model (ARMAX), and output-error (O-E) model structure. This paper presents a new structure, an

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Gupta, Saurav, Subhransu Padhee, and Libor Pekar. "Recursive least squares identification of heat exchanger system using block-structured models." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 236, no. 4 (2021): 870–79. http://dx.doi.org/10.1177/09596518211054921.

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This study provides a recursive parametric identification scheme for a liquid-saturated steam heat exchanger system. The recursive identification scheme uses block-structured Wiener and Hammerstein models as model structure and recursive least squares estimation scheme as the parameter estimation method. The estimated block-oriented model provides higher accuracy of estimation than linear models provided in the literature. From the simulation results, it is observed that the Wiener model can provide 88% goodness-of-FIT, whereas Hammerstein model can provide 96% goodness-of-FIT using the said t

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Cai, Hongbin, Ping Li, Chengli Su, and Jiangtao Cao. "Double-layered nonlinear model predictive control based on Hammerstein–Wiener model with disturbance rejection." Measurement and Control 51, no. 7-8 (2018): 260–75. http://dx.doi.org/10.1177/0020294018785500.

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This paper presents the double-layered nonlinear model predictive control method for a continuously stirred tank reactor and a pH neutralization process that are subject to input disturbances and output disturbances at the same time. The nonlinear systems can be described as a Hammerstein -Wiener model. Furthermore, two nonlinear parts of the Hammerstein -Wiener model should be transformed into linear combination of known input and unknown disturbances, respectively. By taking advantage of Kalman filter, disturbances and states can be estimated. The estimated disturbances and states can be con

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Cao, Qingmei, and Yonghong Tan. "Online Optimization Method for Nonlinear Model-Predictive Control in Angular Tracking for MEMS Micromirror." Micromachines 13, no. 11 (2022): 1867. http://dx.doi.org/10.3390/mi13111867.

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In this brief, a precise angular tracking control strategy using nonlinear predictive optimization control (POC) approach is address. In order to deal with the model uncertainty and noise interference, a online Hammerstein-model-based POC is designed using online estimated parameters and model residual. Above all, a rate-dependent Duhem model is used to describe the nonlinear sub-model of the whole Hammerstein architecture for depicting multi-valued mapping nonlinear characteristic. Then, predictive output of angular deflection is obtained by Diophantine function based on linear submodel. Subs

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Moriyasu, Ryuta, Taro Ikeda, Sho Kawaguchi, and Kenji Kashima. "Structured Hammerstein-Wiener Model Learning for Model Predictive Control." IEEE Control Systems Letters 6 (2022): 397–402. http://dx.doi.org/10.1109/lcsys.2021.3077201.

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Lin, W., and P. X. Liu. "Hammerstein model identification based on bacterial foraging." Electronics Letters 42, no. 23 (2006): 1332. http://dx.doi.org/10.1049/el:20062743.

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Er-Wei Bai and Minyue Fu. "A blind approach to Hammerstein model identification." IEEE Transactions on Signal Processing 50, no. 7 (2002): 1610–19. http://dx.doi.org/10.1109/tsp.2002.1011202.

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Ninness, Brett, and Stuart Gibson. "Quantifying the Accuracy of Hammerstein Model Estimation." IFAC Proceedings Volumes 33, no. 15 (2000): 959–64. http://dx.doi.org/10.1016/s1474-6670(17)39877-4.

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Ninness, Brett, and Stuart Gibson. "Quantifying the accuracy of Hammerstein model estimation." Automatica 38, no. 12 (2002): 2037–51. http://dx.doi.org/10.1016/s0005-1098(02)00101-2.

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Cornoiu, Mihai, and Dominique Beauvois. "Hammerstein Model Identification Using Particle Swarm Optimization." IFAC Proceedings Volumes 45, no. 6 (2012): 1727–32. http://dx.doi.org/10.3182/20120523-3-ro-2023.00400.

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Hou, Jie, Tao Liu, Bo Wahlberg, and Magnus Jansson. "Subspace Hammerstein Model Identification under Periodic Disturbance." IFAC-PapersOnLine 51, no. 15 (2018): 335–40. http://dx.doi.org/10.1016/j.ifacol.2018.09.157.

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