scholarly journals Noise Reduction in the Swept Sine Identification Procedure of Nonlinear Systems

2021 ◽  
Vol 11 (16) ◽  
pp. 7273
Author(s):  
Pietro Burrascano ◽  
Matteo Ciuffetti
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Additive Noise ◽  
Laboratory Data ◽  
Model Identification ◽  
Estimation Procedure ◽  
Parameters Estimation ◽  
Hammerstein Model ◽  
Model Parameters ◽  
Identification Procedure

The Hammerstein model identification technique based on swept sine excitation signals proved in numerous applications to be particularly effective for the definition of a model for nonlinear systems. In this paper we address the problem of the robustness of this model parameter estimation procedure in the presence of noise in the measurement step. The relationship between the different functions that enter the identification procedure is analyzed to assess how the presence of additive noise affects model parameters estimation. This analysis allows us to propose an original technique to mitigate the effects of additive noise in order to improve the accuracy of model parameters estimation. The different aspects addressed in the paper and the technique for mitigating the effects of noise on the accuracy of parameter estimation are verified on both synthetic and experimental data acquired with an ultrasonic system. The results of both simulations and experiments on laboratory data confirm the correctness of the assumptions made and the effectiveness of the proposed mitigation methodology.

A New Integrated Algorithm of Structure Determination and Parameter Estimation for Nonlinear Systems Identification

1999 ◽  
Author(s):  
Wang Xiao Wang ◽  
Jianyin Xie
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Structure Determination ◽  
Model Identification ◽  
Identification Algorithm ◽  
Householder Transformation ◽  
Systems Identification ◽  
Value Decomposition ◽  
Svd Method ◽  
Nonlinear Systems Identification

Abstract A new integrated algorithm of structure determination and parameter estimation is proposed for nonlinear systems identification in this paper, which is based on the Householder Transformation (HT), Givens and Modified Gram-Schmidt (MGS) algorithms. While being used for the polynomial and rational NARMAX model identification, it can select the model terms while deleting the unimportant ones from the assumed full model, avoiding the storage difficulty as the CGS identification algorithm does which is proposed by Billings et. al., and is numerically more stable. Combining the H algorithm with the modified bidiagonalization least squares (MBLS) algorithm and the singular value decomposition (SVD) method respectively, two algorithms referred to as the MBLSHT and SVDHT ones are proposed for the polynomial and rational NARMAX model identification. They are all numerically more stable than the HT or Givens or MGS algorithm given in this paper, and the MBLSHT algorithm has the best performance. A higher precision for the parameter estimation can thus be obtained by them, as supported b simulation results.

Identification of Certain Polynomial Nonlinear Structures by Adaptive Selectively-Sensitive Excitation

1993 ◽  
Vol 115 (3) ◽  
pp. 246-255 ◽  
Author(s):  
Y. Ben-Haim
Keyword(s):  
Vibrational Excitation ◽  
Model Identification ◽  
Elastic Beam ◽  
Estimation Procedure ◽  
Estimation Algorithm ◽  
Model Parameters ◽  
Nonlinear Part ◽  
Precise Knowledge ◽  
Low Dimensional ◽  
Selective Sensitivity

This paper presents a method for identification of certain polynomial nonlinear dynamic systems by adaptive vibrational excitation. The identification is based on the concept of selective sensitivity and is implemented by an adaptive multihypothesis estimation algorithm. The central problem addressed by this method is reduction of the dimensionality of the space in which the model identification is performed. The method of selective sensitivity allows one to design an excitation which causes the response to be selectively sensitive to a small set of model parameters and insensitive to all the remaining model parameters. The identification of the entire system thus becomes a sequence of low-dimensional estimation problems. The dynamical system is modelled as containing both a linear and a nonlinear part. The estimation procedure presumes precise knowledge of the linear model and knowledge of the structure, though not the parameter values, of the nonlinear part of the model. The theory is developed for three different polynomial forms of the nonlinear model: quadratic, cubic and hybrid polynomial nonlinearities. The estimation procedure is illustrated through simulated identification of quadratic nonlinearities in the small-angle vibrations of a uniform elastic beam.

ANALYSIS OF THE INFLUENCE OF A PRIORI UNCERTAINTY RELATING TO THE DISPERSION OF ADDITIVE NOISE ON THE OPERATION OF SIGNAL PARAMETERS ESTIMATION ALGORITHMS

2021 ◽  
Author(s):  
Н.Е. ПОБОРЧАЯ
Keyword(s):  
Nonlinear Filtering ◽  
Additive Noise ◽  
A Priori ◽  
Estimation Procedure ◽  
Parameters Estimation ◽  
Joint Estimation ◽  
Regularizing Algorithm ◽  
Estimation Algorithms ◽  
True Variance ◽  
A Priori Uncertainty

Проведен анализ работы регуляризующего алгоритма и процедуры нелинейной фильтрации в условиях неточного знания величины дисперсии аддитивного шума и анализ их вычислительной сложности. С помощью регуляризующих алгоритмов на фоне аддитивного и фазового шума оценивались параметры сигнала квадратурной амплитудной модуляции: сдвиг частоты, постоянные составляющие квадратур сигнала, амплитудный и фазовый дисбаланс, амплитуда и фаза сигнала. Показано, что их сложность ниже, чем у известной процедуры совместного оценивания, а регуляризующий алгоритм устойчивее процедуры нелинейной фильтрации к отклонению дисперсии аддитивного шума от истинных значений. Analysis of the operation of regularizing algorithm and procedure of nonlinear filtering in conditions of the imprecise value of the variance of the additive noise and analysis of their computational complexity were carried out. Using regularizing these algorithms against the background of additive and phase noise, the following parameters of the quadrature amplitude modulation signal were estimated: frequency shift, constant components of the signal quadrature, amplitude and phase imbalance, amplitude and phase of the signal. It is shown that their complexity is lower than that of the well-known joint estimation procedure, and also that the regularizing algorithm is more resistant to deviations from the true variance of the additive noise than the nonlinear filtering procedure.

scholarly journals Parameter Estimation of a Reliability Model of Demand-Caused and Standby-Related Failures of Safety Components Exposed to Degradation by Demand Stress and Ageing That Undergo Imperfect Maintenance

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
S. Martorell ◽  
P. Martorell ◽  
A. I. Sánchez ◽  
R. Mullor ◽  
I. Martón
Keyword(s):  
Parameter Estimation ◽  
Nuclear Power ◽  
Likelihood Estimation ◽  
Real Data ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Reliability Model ◽  
Imperfect Maintenance ◽  
Maintenance Policies

One can find many reliability, availability, and maintainability (RAM) models proposed in the literature. However, such models become more complex day after day, as there is an attempt to capture equipment performance in a more realistic way, such as, explicitly addressing the effect of component ageing and degradation, surveillance activities, and corrective and preventive maintenance policies. Then, there is a need to fit the best model to real data by estimating the model parameters using an appropriate tool. This problem is not easy to solve in some cases since the number of parameters is large and the available data is scarce. This paper considers two main failure models commonly adopted to represent the probability of failure on demand (PFD) of safety equipment: (1) by demand-caused and (2) standby-related failures. It proposes a maximum likelihood estimation (MLE) approach for parameter estimation of a reliability model of demand-caused and standby-related failures of safety components exposed to degradation by demand stress and ageing that undergo imperfect maintenance. The case study considers real failure, test, and maintenance data for a typical motor-operated valve in a nuclear power plant. The results of the parameters estimation and the adoption of the best model are discussed.

scholarly journals Research and application of parameter estimation method in hydrological model based on dual ensemble Kalman filter

2021 ◽  
Author(s):  
Mengtian Lu ◽  
Sicheng Lu ◽  
Weihong Liao ◽  
Xiaohui Lei ◽  
Zhaokai Yin ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Assimilation Efficiency ◽  
Estimation Method ◽  
Initial Distribution ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Multiple Parameters ◽  
Basin Surface

Abstract Although field measurements and using long hydrological datasets provide a reliable method for parameters' calibration, changes in the underlying basin surface and lack of hydrometeorological data may affect parameter accuracy in streamflow simulation. The ensemble Kalman filter (EnKF) can be used as a real-time parameter correction method to solve this problem. In this study, five representative Xin'anjiang model parameters are selected to study the effects of the initial parameter ensemble distribution and the specific function form of the parameter on EnKF parameter estimation process for both single and multiple parameters. Results indicate: (1) the method of parameter calibration to determine the initial distribution mean can improve the assimilation efficiency; (2) there is mutual interference among the parameters during multiple parameters' estimation which invalidates some conclusions of single-parameter estimation. We applied and evaluated the EnKF method in Jinjiang River Basin, China. Compared to traditional approaches, our method showed a better performance in both basins with long hydrometeorological dataset (an increase of Kling–Gupta efficiency (KGE) from 0.810 to 0.887 and a decrease of bias from −1.08% to −0.74%); and in basins with a lack of hydrometeorological data (an increase of KGE from 0.536 to 0.849 and a decrease of bias from −15.55% to −11.42%).

scholarly journals Online Validation of Relay based Identification and Controller Design with an Anti-Reset Windup for a Binary Distillation Column

2018 ◽  
Vol 7 (4.30) ◽  
pp. 516
Author(s):  
Eadala Sarath Yadav ◽  
Thirunavukkarasu Indiran ◽  
Shanmuga Priya Selvanathan ◽  
Ganesh UG
Keyword(s):  
Control Algorithm ◽  
Distillation Column ◽  
Closed Loop ◽  
Controller Design ◽  
Model Identification ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Trial And Error ◽  
Sustained Oscillations ◽  
Conventional Methods

Sequential auto-tuning based model identification is a closed loop approach, which has an extensive benefit compared to conventional methods because of user defined manipulated gain to the process within the verge of instability. Model parameters estimation of multi input multi output process is challenging because of existence of multivariable interaction among the variables. In this paper ideal relay is used as a sequential basis for binary distillation column in real time. Obtaining sustained oscillations in conventional methods is based on trial and error, benefit of relay is that oscillations can be generated as scaling of user defined gains. Predictive PI control algorithm is implemented. Results depicts the efficiency of methodology and importance of anti-reset term in the algorithm. 

scholarly journals Online Validation of Relay based Identification and Controller Design with an Anti-Reset Windup for a Binary Distillation Column

2018 ◽  
Vol 7 (4.19) ◽  
pp. 983
Author(s):  
Eadala Sarath Yadav ◽  
Thirunavukkarasu Indiran ◽  
Shanmuga Priya Selvanathan ◽  
Ganesh UG
Keyword(s):  
Control Algorithm ◽  
Distillation Column ◽  
Closed Loop ◽  
Controller Design ◽  
Model Identification ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Trial And Error ◽  
Sustained Oscillations ◽  
Conventional Methods

Sequential auto-tuning based model identification is a closed loop approach, which has an extensive benefit compared to conventional methods because of user defined manipulated gain to the process within the verge of instability. Model parameters estimation of multi input multi output process is challenging because of existence of multivariable interaction among the variables. In this paper ideal relay is used as a sequential basis for binary distillation column in real time. Obtaining sustained oscillations in conventional methods is based on trial and error, benefit of relay is that oscillations can be generated as scaling of user defined gains. Predictive PI control algorithm is implemented. Results depicts the efficiency of methodology and importance of anti-reset term in the algorithm.  

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