scholarly journals RECURSIVE UPDATING OF DISTRIBUTED STATE VARIABLES USING OBSERVED DISCHARGES THROUGH KALMAN FILTER

2007 ◽  
Vol 51 ◽  
pp. 67-72 ◽  
Author(s):  
Sunmin KIM ◽  
Yasuto TACHIKAWA ◽  
Kaoru TAKARA
Keyword(s):  
Kalman Filter ◽  
State Variables ◽  
Recursive Updating

scholarly journals Parameters Estimation and Prediction of Water Movement and Solute Transport in Layered, Variably Saturated Soils Using the Ensemble Kalman Filter

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1520
Author(s):  
Zheng Jiang ◽  
Quanzhong Huang ◽  
Gendong Li ◽  
Guangyong Li
Keyword(s):  
Kalman Filter ◽  
Solute Transport ◽  
Ensemble Kalman Filter ◽  
Water Movement ◽  
Parameters Estimation ◽  
Saturated Soils ◽  
Hydrological Models ◽  
State Variables ◽  
Ensemble Size ◽  
Highly Nonlinear

The parameters of water movement and solute transport models are essential for the accurate simulation of soil moisture and salinity, particularly for layered soils in field conditions. Parameter estimation can be achieved using the inverse modeling method. However, this type of method cannot fully consider the uncertainties of measurements, boundary conditions, and parameters, resulting in inaccurate estimations of parameters and predictions of state variables. The ensemble Kalman filter (EnKF) is well-suited to data assimilation and parameter prediction in Situations with large numbers of variables and uncertainties. Thus, in this study, the EnKF was used to estimate the parameters of water movement and solute transport in layered, variably saturated soils. Our results indicate that when used in conjunction with the HYDRUS-1D software (University of California Riverside, California, CA, USA) the EnKF effectively estimates parameters and predicts state variables for layered, variably saturated soils. The assimilation of factors such as the initial perturbation and ensemble size significantly affected in the simulated results. A proposed ensemble size range of 50–100 was used when applying the EnKF to the highly nonlinear hydrological models of the present study. Although the simulation results for moisture did not exhibit substantial improvement with the assimilation, the simulation of the salinity was significantly improved through the assimilation of the salinity and relative solutetransport parameters. Reducing the uncertainties in measured data can improve the goodness-of-fit in the application of the EnKF method. Sparse field condition observation data also benefited from the accurate measurement of state variables in the case of EnKF assimilation. However, the application of the EnKF algorithm for layered, variably saturated soils with hydrological models requires further study, because it is a challenging and highly nonlinear problem.

IMM-UKF-TFS Model-based Approach for Intelligent Navigation

2013 ◽  
Vol 66 (6) ◽  
pp. 859-877 ◽  
Author(s):  
M. Malleswaran ◽  
V. Vaidehi ◽  
S. Irwin ◽  
B. Robin
Keyword(s):  
Kalman Filter ◽  
Constant Velocity ◽  
Unscented Kalman Filter ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
State Variables ◽  
Constant Acceleration ◽  
Positional Accuracy ◽  
Model Based ◽  
Novel Approach

This paper aims to introduce a novel approach named IMM-UKF-TFS (Interacting Multiple Model-Unscented Kalman Filter-Two Filter Smoother) to attain positional accuracy in the intelligent navigation of a manoeuvring vehicle. Here, the navigation filter is designed with an Unscented Kalman Filter (UKF), together with an Interacting Multiple Model algorithm (IMM), which estimates the state variables and handles the noise uncertainty of the manoeuvring vehicle. A model-based estimator named Two Filter Smoothing (TFS) is implemented along with the UKF-based IMM to improve positional accuracy. The performance of the proposed IMM-UKF-TFS method is verified by modelling the vehicle motion into Constant Velocity-Coordinated Turn (CV-CT), Constant Velocity – Constant Acceleration (CV-CA) and Constant Acceleration-Coordinated Turn (CA-CT) models. The simulation results proved that the proposed IMM-UKF-TFS gives better positional accuracy than the existing conventional estimators such as UKF and IMM-UKF.

A Ship Flotation Control System Based on Pump-Driven Tank

2011 ◽  
Author(s):  
Juan Guo ◽  
Meng Tang ◽  
Zaojian Zou
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Structural Damage ◽  
Model Simulation ◽  
Theoretical Calculations ◽  
Wave Disturbance ◽  
Ship Motion ◽  
State Variables ◽  
Initial State ◽  
Asymmetric Loading

Extensive development in ship motion control strategies and systems in recent decades has called for higher requirements in control system accuracy and reliability. In this paper, a ship flotation control system based on pump-driven active tank is established. A special case is discussed, where the ship is heeling under an asymmetric loading either by structural damage or asymmetric consumption of ammunition. The purpose of the control system is to keep the ship in upright floating position in waves by transferring liquid between the tanks. Kalman filter is designed to eliminate the wave disturbance, in order to identify the heeling angle caused by asymmetric loading change. The effect of wave disturbance at different wave encounter angles, wave heights, as well as ship speeds is analyzed. Tuning of filter parameters such as initial state variables, initial error covariance and noise covariance is performed to achieve better filtering performance for different parameters of waves and ship motion. To verify the control model, simulation is conducted for a 3340t ship and the simulation results are compared with the theoretical calculations. The research results show the applicability and efficiency of Kalman filter. The concept of the control system presented in the paper is helpful to improve ship stability and safety when ship upright floating condition is disturbed.

scholarly journals Kalman Filtering Applied to Induction Motor State Estimation

2020 ◽  
Author(s):  
Yassine Zahraoui ◽  
Mohamed Akherraz
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Induction Motor ◽  
Kalman Filtering ◽  
Speed Estimation ◽  
Stochastic Algorithm ◽  
State Variables ◽  
Extended Kalman Filtering ◽  
Filtering Theory ◽  
State Observation

This chapter presents a full definition and explanation of Kalman filtering theory, precisely the filter stochastic algorithm. After the definition, a concrete example of application is explained. The simulated example concerns an extended Kalman filter applied to machine state and speed estimation. A full observation of an induction motor state variables and mechanical speed will be presented and discussed in details. A comparison between extended Kalman filtering and adaptive Luenberger state observation will be highlighted and discussed in detail with many figures. In conclusion, the chapter is ended by listing the Kalman filtering main advantages and recent advances in the scientific literature.

A Model of a Pilot's State Estimation Based on Sequential Instrument Readings

1983 ◽  
Vol 27 (12) ◽  
pp. 964-964
Author(s):  
Marvin C. Waller
Keyword(s):  
Kalman Filter ◽  
Generic Model ◽  
Simulation Studies ◽  
Sequential Process ◽  
State Variables ◽  
Pilot Model ◽  
Preliminary Validation ◽  
Time Histories ◽  
Filter Type ◽  
Single Instrument

A study has been completed at LaRC which applies a generic model of an operator, the Human Operator Simulator (HOS), to the representation of a pilot flying an advanced transport airplane. The model has been demonstrated to generate results which closely match those obtained in fixed based, hardware oriented, piloted simulation studies. The model generates lookpoint predictions as could be measured with an oculometer system as well as time histories of state variables of the airplane and pilot control inputs, and provides a means of estimating pilot workload. The model incorporates a Kalman filter type estimator to represent the pilot's knowledge of the airplane's state, and his sequential process of updating this knowledge by absorbing information from different display sources during the course of a flight. This presentation provides a brief description of the HOS based pilot model and presents in some detail, implementation in the model of the algorithm to represent the pilot's knowledge of the state. The algorithm used allows representation of the effects of reading a single instrument at a given time. Results of some preliminary validation and demonstration steps are presented. Also, elements of a plausible pilot (or operator) scanning model, which is a spin-off of the estimation process, are discussed.

Engine Parameter Estimation in Test Cells Using Hybrid Physics/Empirical Models

2011 ◽  
Author(s):  
Jonathan A. DeCastro ◽  
Dean K. Frederick ◽  
Liang Tang
Keyword(s):  
Kalman Filter ◽  
Hybrid Model ◽  
Dynamic Performance ◽  
Health Condition ◽  
State Variables ◽  
Engine Model ◽  
Physical Plant ◽  
Highly Nonlinear ◽  
Test Cells ◽  
Estimation System

Estimation of engine parameters such as thrust in test cells is a difficult process due to the highly nonlinear nature of the engine dynamics, the complex interdependency of thrust and the engine’s health condition, and factors that corrupt thrust measurements due to test stand construction. Because the frequency content of the corrupting dynamics is close to the engine’s dynamics, filtering the thrust signal is not sufficient for extraction of the true dynamic content. A configurable thrust estimation system is developed for accurate data reduction which provides “virtual” measurements of thrust and other necessary parameters at steady state and during aggressive engine transients. The thrust estimation framework consists of a representative nonlinear engine model coupled with an adaptive structural dynamics model. To account for discrepancies between the physics-based model and the true engine, a hybrid model using a novel neural network (NN) enhancement to a physics-based engine model is presented that reduces certain modeling errors between the engine model and the physical plant. This includes engine-to-engine variation, engine degradation and any essential neglected dynamics. To fuse the model and sensor measurements, this hybrid model is used within a constant-gain extended Kalman filter batch estimator which is able to reconstruct the true dynamic performance of the engine using noisy or corrupted sensor measurements and control inputs. The Kalman filter estimates measured and unmeasured parameters and state variables such as engine component deterioration parameters and effective flow areas.

On-Line Performance Monitoring and Engine Diagnostic Using Robust Kalman Filtering Techniques

2003 ◽  
Author(s):  
Pierre Dewallef ◽  
Olivier Le´onard
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Kalman Filters ◽  
Performance Monitoring ◽  
Stochastic Methods ◽  
Inlet Temperature ◽  
State Variables ◽  
Present Contribution ◽  
Regression Problem ◽  
On Line

In this contribution, an on-line engine performance monitoring is carried out through an engine health parameter estimation based on several gas path measurements. This health parameter estimation makes use of the analytical redundancy of an engine model and therefore implies the knowledge of the engine state. As the latter is a priori not known the second task is therefore an engine state variable estimation. State variables here designate working conditions such as inlet temperature, pressure, Mach number, rotational speeds, … Estimation of the state variables constitutes a general application of the Extended Kalman Filter theory, while the health parameter estimation is a classical recurrent regression problem. Recent advances in stochastic methods [1] show that both problems can be solved by two Kalman filters working jointly. Such filters are usually named Dual Kalman Filters. The present contribution aims at using a dual Kalman filter modified to provide robustness. This procedure should be able to cope with as much as 20 to 30% of faulty data. The resulting online method is applied to a turbofan model developed in the frame of the OBIDICOTE 1 project. Several tests are carried out to check the performance monitoring capability and the robustness that can be achieved.

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