Real-time simultaneous input-state-parameter estimation with modulated colored noise excitation

2022 ◽  
Vol 165 ◽  
pp. 108378
Author(s):  
Ke Huang ◽  
Ka-Veng Yuen ◽  
Lei Wang
Keyword(s):  
Parameter Estimation ◽  
Real Time ◽  
Colored Noise ◽  
State Parameter ◽  
Input State ◽  
Simultaneous Input

Input-state-parameter estimation of structural systems from limited output information

2019 ◽  
Vol 126 ◽  
pp. 711-746 ◽  
Author(s):  
V.K. Dertimanis ◽  
E.N. Chatzi ◽  
S. Eftekhar Azam ◽  
C. Papadimitriou
Keyword(s):  
Parameter Estimation ◽  
State Parameter ◽  
Structural Systems ◽  
Input State ◽  
Limited Output ◽  
Output Information

Real-time thermal dynamic analysis of a house using RC models and joint state-parameter estimation

2020 ◽  
pp. 107184
Author(s):  
Yong Li ◽  
Juan Castiglione ◽  
Rodrigo Astroza ◽  
Yuxiang Chen
Keyword(s):  
Parameter Estimation ◽  
Dynamic Analysis ◽  
Real Time ◽  
State Parameter ◽  
Joint State

scholarly journals Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 750
Author(s):  
Wenkang Wan ◽  
Jingan Feng ◽  
Bao Song ◽  
Xinxin Li
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Degrees Of Freedom ◽  
Unscented Kalman Filter ◽  
State Parameter ◽  
State Observation ◽  
Straight Line ◽  
Distributed Drive Electric Vehicle ◽  
Huber Method ◽  
Yaw Moment

Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy.

scholarly journals Real time cascade adaptive notch filter scheme for sinusoidal parameter estimation

1994 ◽  
Vol 39 (1-2) ◽  
pp. 117-130 ◽  
Author(s):  
Soo-Chang Pei ◽  
Chien-Cheng Tseng
Keyword(s):  
Parameter Estimation ◽  
Real Time ◽  
Notch Filter ◽  
Adaptive Notch Filter

scholarly journals Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments

2011 ◽  
Vol 15 (8) ◽  
pp. 2437-2457 ◽  
Author(s):  
S. Nie ◽  
J. Zhu ◽  
Y. Luo
Keyword(s):  
Parameter Estimation ◽  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Estimation Method ◽  
State Parameter ◽  
Hydraulic Parameters ◽  
Constrained Estimation ◽  
Estimated Parameters

Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, soil moisture initial condition and atmospheric forcing. A physically based land surface model is used for this purpose. Using a series of identical twin experiments in two kinds of initial parameter distribution (IPD) scenarios, the narrow IPD (NIPD) scenario and the wide IPD (WIPD) scenario, model-generated near surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter) in the model. The estimation of single imperfect parameter is successful with the ensemble mean value of all three estimated parameters converging to their true values respectively in both NIPD and WIPD scenarios. Increasing the number of imperfect parameters leads to a decline in the estimation performance. A wide initial distribution of estimated parameters can produce improved simultaneous multi-parameter estimation performances compared to that of the NIPD scenario. However, when the number of estimated parameters increased to three, not all parameters were estimated successfully for both NIPD and WIPD scenarios. By introducing constraints between estimated hydraulic parameters, the performance of the constrained three-parameter estimation was successful, even if temporally sparse observations were available for assimilation. The constrained estimation method can reduce RMSE much more in soil moisture forecasting compared to the non-constrained estimation method and traditional non-parameter-estimation assimilation method. The benefit of this method in estimating all imperfect parameters simultaneously can be fully demonstrated when the corresponding non-constrained estimation method displays a relatively poor parameter estimation performance. Because all these constraints between parameters were obtained in a statistical sense, this constrained state-parameter estimation scheme is likely suitable for other land surface models even with more imperfect parameters estimated in soil moisture assimilation applications.

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