Identification of Time-Varying Hammerstein-Wiener Systems

Yu, Feng; Mao, Zhizhong; He, Dakuo

doi:10.1109/access.2020.3011608

Cited by 7 publications

(5 citation statements)

References 43 publications

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“…For ease of understanding, the details regarding the derivation of equations ( 16)- (24) are given as follows:…”

Section: Appendixmentioning

confidence: 99%

“…Based on the concept of pre-estimation of system parameters, Ciolek et al [23] proposed a decoupled KF approach for the estimation of time-varying systems. To enhance the convergence, Yu et al [24] proposed a modifed EKF for identifying time-variant Hammerstein-Wiener nonlinear systems. More recently, the KF-based online identifcation of cable force [25], precast segmental columns [26], or sensor fault [27] was also conducted.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous Identification of Time-Varying Parameters and External Loads Based on Extended Kalman Filter: Approach and Validation

Zhang

Hua

et al. 2023

Structural Control and Health Monitoring

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Online identification of time-variant parameters without knowledge of external loads is an important but challenging task for structural health monitoring and vibration control. In this study, a two-stage approach, named extended Kalman filter with forgetting factor matrix under unknown inputs (EKF-FFM-UI), is proposed for simultaneously identifying the time-variant parameters and external loads. In stage 1, an extended Kalman filter under unknown inputs (EKF-UI) approach previously proposed by the authors is employed for estimating the structural states and unknown loads. This EKF-UI approach is solely suitable for time-invariant system identification. Therefore, the aim of stage 2 is to improve this approach for the purpose of possessing tracking capability. In this stage, the acceleration responses are first reconstructed by using the differential equation of motion and employed for improving the accuracy of estimated structural states. A forgetting factor matrix is introduced into the priori estimation error covariance matrix to track time-varying parameters. The square errors between the measurements and the corresponding estimates are defined as an index and used for detecting the damage time instant. Then, a covariance resetting technique is employed to assure that such changes in structural parameters can be efficiently captured. A shear-type building structure without/with magneto-rheological (MR) dampers and a fixed beam structure are used as numerical examples for validating the effectiveness of the proposed approach. Experimental tests on a six-story building model are also conducted. Results show the time-varying parameters and unknown inputs can be simultaneously identified with acceptable accuracy.

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“…For ease of understanding, the details regarding the derivation of equations ( 16)- (24) are given as follows:…”

Section: Appendixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simultaneous Identification of Time-Varying Parameters and External Loads Based on Extended Kalman Filter: Approach and Validation

Zhang

Hua

et al. 2023

Structural Control and Health Monitoring

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“…Better modeling requires not only approximating the nonlinear function accurately but also simplifying the identification process. In the literature, several researches are restrictive to a polynomial form of the input and/or output nonlinear blocks or well-known input and/or output nonlinear characteristics (such as dead zone or backlash) but with unknown parameters [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][63][64][65][66][67][68][69][70][71][72][73][74].…”

Section: Fuzzy-type Stochastic Output-error Autoregressive Hammerstein-wiener (Fsoeahw) Modelmentioning

confidence: 99%

An Improved Method for Stochastic Nonlinear System’s Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques

et al. 2021

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This paper proposes an innovative identification approach of nonlinear stochastic systems using Hammerstein–Wiener (HW) model with output-error autoregressive (OEA) noise. Two fuzzy systems are suggested for the identification of the input and output nonlinear blocks of a proposed model from given input-output data measurements. In this work, the need for the commonly used assumptions including well-known structure of input and/or output nonlinearities and/or reversible nonlinear output is eliminated by replacing the intermediate variables and noise with their estimates. Four parametric estimation algorithms to identify the proposed fuzzy-type stochastic output-error autoregressive HW (FSOEAHW) model are derived based on backpropagation algorithm and multi-innovation and data filtering identification techniques. The proposed algorithms are improved backpropagation gradient (IBPG) algorithm, multi-innovation IBPG (MIIBPG) algorithm, a data filtering IBPG (FIBPG) algorithm, and a multi-innovation-based FIBPG (MIFIBPG) algorithm. The convergence of the parameter estimation algorithms is studied. The effectiveness of the proposed algorithms is shown by a given simulation example.

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“…(4) Adaptive estimation methods (e.g., Nordsjö & Zetterberg (2001), Vӧrös (2005Vӧrös ( , 2017, which use nonlinear filtering or recursive estimation techniques to estimate TV Hammerstein parameters online as the system operates (as opposed to offline temporal expansion). A recent notable example is Yu et al (2020), who developed a recursive method based on Extended Kalam Filter (EKF) to estimate the time trajectories of the parameters of TV Hammerstein-Wiener systems. However, a disadvantage of all adaptive methods is that the time course of the adaptive changes may not match that of the system parameters since the adaptive methods have their own dynamics.…”

Section: Introductionmentioning

confidence: 99%

A Non-Parametric Approach for Identification of Parameter Varying Hammerstein Systems

Tehrani

Kearney

2022

IEEE Access

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This paper presents a novel method for non-parametric identification of parameter-varying (PV) Hammerstein systems where the parameters of both the static nonlinearity and the linear dynamics change with a scheduling variable. The proposed method estimates the individual elements of the PV Hammerstein system by describing the static nonlinear element using a PV Chebychev basis expansion and the linear dynamic element as a non-parametric PV impulse response function with Laguerre basis expansion. The method was validated using Monte-Carlo simulations of a PV Hammerstein model of ankle reflex stiffness during large movements. Results demonstrated that the method is simple, effective, and robust; it accurately identified the PV Hammerstein system in the presence of relatively large colored, time-varying measurement noise (average SNR of 15dB). These results demonstrate the two main contributions of the method: (1) It accurately and precisely estimates both the linear and nonlinear elements of the PV Hammerstein cascade as they vary with a scheduling variable; and (2) Models identified with the method accurately predicts the response of the PV Hammerstein system to novel scheduling variable trajectories. To our knowledge, no other Hammerstein method can achieve these.

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Identification of Time-Varying Hammerstein-Wiener Systems

Cited by 7 publications

References 43 publications

Simultaneous Identification of Time-Varying Parameters and External Loads Based on Extended Kalman Filter: Approach and Validation

Simultaneous Identification of Time-Varying Parameters and External Loads Based on Extended Kalman Filter: Approach and Validation

An Improved Method for Stochastic Nonlinear System’s Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques

A Non-Parametric Approach for Identification of Parameter Varying Hammerstein Systems

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