Tuning of Computer Model Parameters in Managed-Pressure Drilling Applications Using an Unscented-Kalman-Filter Technique

SPE Journal ◽  
2010 ◽  
Vol 15 (03) ◽  
pp. 856-866 ◽  
Author(s):  
Jan Einar Gravdal ◽  
Rolf J. Lorentzen ◽  
Kjell K. Fjelde ◽  
Erlend H. Vefring
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Synthetic Data ◽  
Pressure Profile ◽  
Model Parameters ◽  
High Pressure High Temperature ◽  
Managed Pressure Drilling ◽  
High Degree ◽  
Advanced Computer

Summary To manage the annular pressure profile during managed-pressure drilling (MPD) operations, simulations performed with advanced computer models are needed. To obtain a high degree of accuracy in these simulations, it is crucial that all parameters describing the system are as correct as possible. A new methodology for real-time updating of key parameters in a well-flow model by taking into account real-time measurements, including measuring uncertainty, is presented. Key model parameters are tuned using a recently developed estimation technique based on the traditional Kalman filter. The presented methodology leads to a more-accurate prediction of well-flow scenarios. Although the present study is motivated by applications in MPD, the idea of tuning model parameters should be of great importance in a wide area of applications. The performance of the filter is studied, using both synthetic data and real measurements from a North Sea high-pressure/high-temperature (HP/HT) drilling operation. Benefits by this approach are seen in more-accurate downhole-pressure predictions, which are of major importance for safety and economic reasons during MPD operations.

scholarly journals A flexible framework for sequential estimation of model parameters in computational hemodynamics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Christopher J. Arthurs ◽  
Nan Xiao ◽  
Philippe Moireau ◽  
Tobias Schaeffter ◽  
C. Alberto Figueroa
Keyword(s):  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Sequential Estimation ◽  
Wall Material ◽  
Patient Specific ◽  
Model Parameters ◽  
Computational Framework ◽  
Lumped Parameter ◽  
Estimation Of Model Parameters

AbstractA major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A “Netlist” implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package.

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.

Real-Time Signal Denoising Algorithm in Wheel Force Transducer

2014 ◽  
Vol 615 ◽  
pp. 244-247
Author(s):  
Dong Wang ◽  
Guo Yu Lin ◽  
Wei Gong Zhang
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Extended Kalman Filter ◽  
Unscented Kalman Filter ◽  
Force Transducer ◽  
Time Signal ◽  
Signal Denoising ◽  
Observation Function ◽  
Simulation Results ◽  
Wheel Force Transducer

The wheel force transducer (WFT) is used to measure dynamic wheel loads. Unlike other force sensors, WFT is rotating with the wheel. For this reason, the outputs and the inputs of the transducer are nonlinearly related, and traditional Kalman Filter is not suitable. In this paper, a new real-time filter algorithm utilizing Quadrature Kalman Filter (QKF) is proposed to solve this problem. In Quadrature Kalman Filter, Singer model is introduced to track the wheel force, and the observation function is established for WFT. The simulation results illustrate that the new filter outperforms the traditional Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF).

scholarly journals Battery state of charge estimation and online wheel slip ratio control for autonomous wheeled mobile robot

2021 ◽  
Author(s):  
Maral Partovibakhsh
Keyword(s):  
Kalman Filter ◽  
Power Consumption ◽  
Mobile Robot ◽  
Unscented Kalman Filter ◽  
Longitudinal Velocity ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Slip Ratio ◽  
Soc Estimation ◽  
Increasing Function

For autonomous mobile robots moving in unknown environment, accurate estimation of available power along with the robot power demand for each mission is paramount to successful completion of that mission. Regarding the power consumption, the control unit deals with two tasks simultaneously: 1) it has to monitor the power supply (batteries) state of charge (SoC) constantly. This leads to estimation of robot current available power. Besides, batteries are sensitive to deep discharge or overcharge. The battery SoC is an essential factor in power management of a mobile robot. Accurate estimation of the battery SoC can improve power management, optimize the performance, extend the lifetime, and prevent permanent damage to the batteries. 2) The dynamic characteristics of the terrain the robot traverse requires rapid online modifications in its behaviour. The power required for driving a wheel is an increasing function of its slip ratio. For a wheeled robot moving for driving a wheel is an increasing function of its slip ratio. For a wheeled robot moving on different terrains, slip of the wheels should be checked and compensated for to keep the robot moving with less power consumption. To reduce the power consumption, the target robot moving with less power consumption. To reduce the power consumption, the target of the control system is to keep the slip ratio of the driving wheels around the desired value of the control system is to keep the slip ratio of the driving wheels around the desired value. To fulfill the above mentioned tasks, in this thesis, to increase model validity of lithium-ion battery in various charge/discharge scenarios during the mobile robot operation, the battery capacity fade and internal resistance change are modeled by adding them as state variables to a state space model. Using the output measured data, adaptive unscented Kalman Filter (AUKF) is employed for online model parameters identification of the equivalent circuit model at each sampling time. Subsequently, based on the updated model parameters, SoC estimation is conducted using AUKF. The effectiveness of the proposed method is verified through experiments under different power duties in the lab environment through experiments under different power duties in the lab environment. Better results are obtained both in battery model parameters estimation and the battery SoC estimation in comparison with other Kalman filter extensions. Furthermore, for effective control of the slip ratio, a model-based approach to estimating the longitudinal velocity of the mobile robot is presented. The AUKF is developed to estimate the vehicle longitudinal velocity and the wheel angular velocity using measurements from wheel encoders. Based on the estimated slip ratio, a sliding mode controller is designed for slip control of the uncertain nonlinear dynamical system in the presence of model uncertainties, parameter variations, and disturbances. Experiments are carried out in real time on a four-wheel mobile robot to verify the effectiveness of the estimation algorithm and the controller. It is shown that the controller is able to control the slip ratio of the mobile robot on different terrains while adaptive concept of AUKF leads to better results than the unscented Kalman filter in estimating the vehicle velocity which is difficult to measure in actual practice.

scholarly journals Moving State Marine SINS Initial Alignment Based on High Degree CKF

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yong-Gang Zhang ◽  
Yu-Long Huang ◽  
Zhe-Min Wu ◽  
Ning Li
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Alignment Method ◽  
Misalignment Angle ◽  
Initial Alignment ◽  
Central Difference ◽  
Angle Condition ◽  
Cubature Kalman Filter ◽  
High Degree ◽  
Central Difference Kalman Filter

A new moving state marine initial alignment method of strap-down inertial navigation system (SINS) is proposed based on high-degree cubature Kalman filter (CKF), which can capture higher order Taylor expansion terms of nonlinear alignment model than the existing third-degree CKF, unscented Kalman filter and central difference Kalman filter, and improve the accuracy of initial alignment under large heading misalignment angle condition. Simulation results show the efficiency and advantage of the proposed initial alignment method as compared with existing initial alignment methods for the moving state SINS initial alignment with large heading misalignment angle.

scholarly journals Orbit Propagation and Determination of Low Earth Orbit Satellites

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Ho-Nien Shou
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Nonlinear Problems ◽  
Low Earth Orbit ◽  
Earth Orbit ◽  
Positioning Accuracy ◽  
Orbit Propagation

This paper represents orbit propagation and determination of low Earth orbit (LEO) satellites. Satellite global positioning system (GPS) configured receiver provides position and velocity measures by navigating filter to get the coordinates of the orbit propagation (OP). The main contradictions in real-time orbit which is determined by the problem are orbit positioning accuracy and the amount of calculating two indicators. This paper is dedicated to solving the problem of tradeoffs. To plan to use a nonlinear filtering method for immediate orbit tasks requires more precise satellite orbit state parameters in a short time. Although the traditional extended Kalman filter (EKF) method is widely used, its linear approximation of the drawbacks in dealing with nonlinear problems was especially evident, without compromising Kalman filter (unscented Kalman Filter, UKF). As a new nonlinear estimation method, it is measured at the estimated measurements on more and more applications. This paper will be the first study on UKF microsatellites in LEO orbit in real time, trying to explore the real-time precision orbit determination techniques. Through the preliminary simulation results, they show that, based on orbit mission requirements and conditions using UKF, they can satisfy the positioning accuracy and compute two indicators.

scholarly journals Online Parameter Identification and State of Charge Estimation of Battery Based on Multitimescale Adaptive Double Kalman Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Wenxian Duan ◽  
Chuanxue Song ◽  
Yuan Chen ◽  
Feng Xiao ◽  
Silun Peng ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Least Square Method ◽  
State Of Charge ◽  
Least Square ◽  
Accurate Estimation ◽  
Battery Life ◽  
Model Parameters ◽  
Simulation Results

An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent.

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