Model-Based Control of Electroslag Remelting Process Using Unscented Kalman Filter

2009 ◽  
Vol 132 (1) ◽  
Author(s):  
Seokyoung Ahn ◽  
Joseph J. Beaman ◽  
Rodney L. Williamson ◽  
David K. Melgaard
Keyword(s):  
Kalman Filter ◽  
Temperature Distribution ◽  
Controller Design ◽  
Unscented Kalman Filter ◽  
Liquid Pool ◽  
Electroslag Remelting ◽  
Superior Performance ◽  
Process Conditions ◽  
Highly Nonlinear ◽  
Process Disturbances

Electroslag remelting (ESR) is used widely throughout the specialty metals industry. The process generally consists of a regularly shaped electrode, wherein a small amount is immersed in liquid slag at a temperature higher than the melting temperature of the electrode. Melting droplets from the electrode fall through the lower density slag into a liquid pool constrained by a crucible and solidify into an ingot. High quality ingots require that electrode melt rate and immersion depth be controlled at all times during the process. This can be difficult when process conditions are such that the temperature distribution in the electrode is not at steady state. This condition is encountered during the beginning and closing stages of the ESR process and also during some process disturbances such as when the melt zone passes through a transverse electrode crack. To address these transient melting situations, a new method of the ESR estimation and control has been developed that incorporates an accurate, reduced order melting model to continually estimate the temperature distribution in the electrode. The ESR process is highly nonlinear, noisy, and has coupled dynamics. An extended Kalman filter and an unscented Kalman filter were chosen as possible estimators and compared in the controller design. During the highly transient periods in melting, the unscented Kalman filter showed superior performance for estimating and controlling the system.

Model-Based Control of Electroslag Remelting Process Using Unscented Kalman Filter

2008 ◽  
Author(s):  
Seokyoung Ahn ◽  
Joseph J. Beaman ◽  
Rodney L. Williamson ◽  
David K. Melgaard
Keyword(s):  
Kalman Filter ◽  
Temperature Distribution ◽  
Controller Design ◽  
Unscented Kalman Filter ◽  
Liquid Pool ◽  
Electroslag Remelting ◽  
Superior Performance ◽  
Process Conditions ◽  
Reduced Order ◽  
Highly Nonlinear

Electroslag Remelting (ESR) is used widely throughout the specialty metals industry. The process generally consists of a regularly shaped electrode that is immersed a small amount in liquid slag at a temperature higher than the melting temperature of the electrode. Melting droplets from the electrode fall through the lower density slag into a liquid pool constrained by a crucible and solidify into an ingot. High quality ingots require that electrode melt rate and immersion depth be controlled. This can be difficult when process conditions are such that the temperature distribution in the electrode is not at steady state. A new method of ESR control has been developed that incorporates an accurate, reduced-order melting model to continually estimate the temperature distribution in the electrode. The ESR process is highly nonlinear, noisy, and has coupled dynamics. An extended Kalman filter and an unscented Kalman filter were chosen as possible estimators and compared in the controller design. During the highly transient periods in melting, the unscented Kalman filter showed superior performance for estimating and controlling the system.

scholarly journals Application of Kalman Filtering Methods to Online Real-Time Structural Identification: A Comparison Study

2016 ◽  
Vol 16 (06) ◽  
pp. 1550016 ◽  
Author(s):  
Mohsen Askari ◽  
Jianchun Li ◽  
Bijan Samali
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Structural Parameters ◽  
Measurement Data ◽  
Linear Structure ◽  
Structural Identification ◽  
Input Output ◽  
Output Measurement ◽  
Highly Nonlinear

System identification refers to the process of building or improving mathematical models of dynamical systems from the observed experimental input–output data. In the area of civil engineering, the estimation of the integrity of a structure under dynamic loadings and during service condition has become a challenge for the engineering community. Therefore, there has been a great deal of attention paid to online and real-time structural identification, especially when input–output measurement data are contaminated by high-level noise. Among real-time identification methods, one of the most successful and widely used algorithms for estimation of system states and parameters is the Kalman filter and its various nonlinear extensions such as extended Kalman filter (EKF), Iterated EKF (IEKF), the recently developed unscented Kalman filter (UKF) and Iterated UKF (IUKF). In this paper, an investigation has been carried out on the aforementioned techniques for their effectiveness and efficiencies through a highly nonlinear single degree of freedom (SDOF) structure as well as a two-storey linear structure. Although IEKF is an improved version of EKF, results show that IUKF generally produces better results in terms of structural parameters and state estimation than UKF and IEKF. Also IUKF is more robust to noise levels compared to the other approaches.

Three-Dimensional Submarine-to-Submarine Passive Target Tracking in the Presence of Non-Gaussian Noises

2021 ◽  
Vol 17 (3) ◽  
pp. 1-24
Author(s):  
Kavitha Lakshmi M. ◽  
Koteswara Rao S. ◽  
Subrahmanyam Kodukula
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Gaussian Noise ◽  
Target Location ◽  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Noise Environment ◽  
Cubature Kalman Filter ◽  
Highly Nonlinear ◽  
Non Gaussian

In underwater surveillance, three-dimensional target tracking is a challenging task. The angles-only measurements (i.e., bearing and elevation) obtained by hull mounted sensors are considered to appraise the target motion parameter. Due to noise in measurements and nonlinearity of the system, it is very hard to find out the target location. For many applications, UKF is best estimator that remaining algorithms. Recently, cubature Kalman filter (CKF) is also popular. It is proposed to use UKF (unscented Kalman filter) and CKF (cubature Kalman filter) algorithms that minimize the noise in measurements. So far, researchers carried out this work (target tracking) in Gaussian noise environment, whereas in this paper same work is carried out for non-Gaussian noise environment. The performance evaluation of the filters using Monte-Carlo simulation and Cramer-Rao lower bound (CRLB) is accomplished and the results are analyzed. Result shows that UKF is well suitable for highly nonlinear systems than CKF.

scholarly journals Complexities in Assessing Structural Health of Civil Infrastructures

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Abdullah Al-Hussein ◽  
Achintya Haldar
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Health Assessment ◽  
Time History ◽  
Dynamic Responses ◽  
Simple Problem ◽  
Civil Infrastructures ◽  
Duration Time ◽  
Highly Nonlinear ◽  
Global Iteration

The complexity in the health assessment of civil infrastructures, as it evolves over a long period of time, is briefly discussed. A simple problem can become very complex based on the current needs, sophistication required, and the technological advancements. To meet the current needs of locating defect spots and their severity accurately and efficiently, infrastructures are represented by finite elements. To increase the implementation potential, the stiffness parameters of all the elements are identified and tracked using only few noise-contaminated dynamic responses measured at small part of the infrastructure. To extract the required information, Kalman filter concept is integrated with other numerical schemes. An unscented Kalman filter (UKF) concept is developed for highly nonlinear dynamic systems. It is denoted as 3D UKF-UI-WGI. The basic UKF concept is improved in several ways. Instead of using one long duration time-history in one global iteration, very short duration time-histories and multiple global iterations with weight factors are used to locate the defect spot more accurately and efficiently. The capabilities of the procedure are demonstrated with the help of two informative examples. The proposed procedure is much superior to the extended Kalman filter-based procedures developed by the team earlier.

Comparison of Unscented and Extended Kalman Filters with Application in Vehicle Navigation

2016 ◽  
Vol 70 (2) ◽  
pp. 411-431 ◽  
Author(s):  
Cheng Yang ◽  
Wenzhong Shi ◽  
Wu Chen
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Field Tests ◽  
Superior Performance ◽  
Vehicle Navigation ◽  
Prototype Model ◽  
Computationally Efficient ◽  
Theoretical Formulation ◽  
Nonlinear State Estimation

The Unscented Kalman Filter (UKF) is a well-known nonlinear state estimation method. It shows superior performance at nonlinear estimation compared to the Extended Kalman Filter (EKF). This paper is devoted to an investigation between UKF and EKF with different feedback control modes in vehicle navigation. Theoretical formulation, simulation and field tests have been carried out to compare the performance of UKF and EKF. The simulation and test results demonstrate that the estimated state of a UKF relies on the measurements and is less sensitive to historical model information. The results also indicate that UKF has benefits for prototype model design due to avoidance of calculation of a Jacobian matrix. EKF, however, is more computationally efficient and more stable.

scholarly journals A generic multi-sensor fusion scheme for localization of autonomous platforms using moving horizon estimation

2021 ◽  
pp. 014233122110114
Author(s):  
Mostafa Osman ◽  
Mohamed W Mehrez ◽  
Mohamed A Daoud ◽  
Ahmed Hussein ◽  
Soo Jeon ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Optimization Problems ◽  
Unscented Kalman Filter ◽  
Computational Cost ◽  
Intelligent Vehicles ◽  
Superior Performance ◽  
Moving Horizon Estimation ◽  
Missing Measurements ◽  
Fusion Scheme

In this paper, a generic multi-sensor fusion framework is developed for the localization of intelligent vehicles and mobile robots. The localization framework is based on moving horizon estimation (MHE). Unlike the commonly used probabilistic filtering algorithms – for example, extended Kalman filter (EKF) and unscented Kalman filter (UKF) – MHE relies on solving successive least squares optimization problems over the innovation of multiple sensors’ measurements and a specific estimation horizon. In this paper, we present an efficient and generic multi-sensor fusion scheme, based on MHE. The proposed multi-sensor fusion scheme is capable of operating with different sensors’ rates, missing measurements, and outliers. Moreover, the proposed scheme is based on a multi-threading architecture to reduce its computational cost, making it more feasible for practical applications. The MHE fusion method is tested using simulated data as well as real experimental data sequences from an intelligent vehicle and a mobile robot combining measurements from different sensors to get accurate localization results. The performance of MHE is compared against that of UKF, where the MHE estimation results show superior performance.

Geomagnetic orbit determination: EKF or UKF?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jin Wu ◽  
Ming Liu ◽  
Chengxi Zhang ◽  
Yulong Huang ◽  
Zebo Zhou
Keyword(s):  
Kalman Filter ◽  
Orbit Determination ◽  
Unscented Kalman Filter ◽  
Nonlinear Models ◽  
Computational Time ◽  
Content Type ◽  
Spacecraft Navigation ◽  
Highly Nonlinear ◽  
Autonomous Orbit Determination ◽  
Spherical Harmonic Models

Purpose Autonomous orbit determination using geomagnetic measurements is an important backup technique for safe spacecraft navigation with a mere magnetometer. The geomagnetic model is used for the state estimation of orbit elements, but this model is highly nonlinear. Therefore, many efforts have been paid to developing nonlinear filters based on extended Kalman filter (EKF) and unscented Kalman filter (UKF). This paper aims to analyze whether to use UKF or EKF in solving the geomagnetic orbit determination problem and try to give a general conclusion. Design/methodology/approach This paper revisits the problem and from both the theoretical and engineering results, the authors show that the EKF and UKF show identical estimation performances in the presence of nonlinearity in the geomagnetic model. Findings While EKF consumes less computational time, the UKF is computationally inefficient but owns better accuracy for most nonlinear models. It is also noted that some other navigation techniques are also very similar with the geomagnetic orbit determination. Practical implications The intrinsic reason of such equivalence is because of the orthogonality of the spherical harmonics which has not been discovered in previous studies. Thus, the applicability of the presented findings are not limited only to the major problem in this paper but can be extended to all those schemes with spherical harmonic models. Originality/value The results of this paper provide a fact that there is no need to choose UKF as a preferred candidate in orbit determination. As UKF achieves almost the same accuracy as that of EKF, its loss in computational efficiency will be a significant obstacle in real-time implementation.

scholarly journals An Accurate State of Charge Estimation Method for Lithium Iron Phosphate Battery Using a Combination of an Unscented Kalman Filter and a Particle Filter

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4536 ◽  
Author(s):  
Thanh-Tung Nguyen ◽  
Abdul Basit Khan ◽  
Younghwi Ko ◽  
Woojin Choi
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Lithium Iron Phosphate ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Iron Phosphate ◽  
State Of Charge ◽  
Superior Performance ◽  
Accurate Estimation ◽  
Soc Estimation

An accurate state of charge (SOC) estimation of the battery is one of the most important techniques in battery-based power systems, such as electric vehicles (EVs) and energy storage systems (ESSs). The Kalman filter is a preferred algorithm in estimating the SOC of the battery due to the capability of including the time-varying coefficients in the model and its superior performance in the SOC estimation. However, since its performance highly depends on the measurement noise (MN) and process noise (PN) values, it is difficult to obtain highly accurate estimation results with the battery having a flat plateau OCV (open-circuit voltage) area in the SOC-OCV curve, such as the Lithium iron phosphate battery. In this paper, a new integrated estimation method is proposed by combining an unscented Kalman filter and a particle filter (UKF-PF) to estimate the SOC of the Lithium iron phosphate battery. The equivalent circuit of the battery used is composed of a series resistor and two R-C parallel circuits. Then, it is modeled by a second-order autoregressive exogenous (ARX) model, and the parameters are identified by using the recursive least square (RLS) identification method. The validity of the proposed algorithm is verified by comparing the experimental results obtained with the proposed method and the conventional methods.

A novel metaheuristic model-based approach for accurate online broken bar fault diagnosis in induction motor using unscented Kalman filter and ant lion optimizer

2019 ◽  
Vol 42 (8) ◽  
pp. 1537-1546 ◽  
Author(s):  
Marouane Rayyam ◽  
Malika Zazi
Keyword(s):  
Kalman Filter ◽  
Fault Diagnosis ◽  
Unscented Kalman Filter ◽  
Superior Performance ◽  
Simultaneous Estimation ◽  
Estimation Errors ◽  
Model Based ◽  
Ant Lion Optimizer ◽  
Detection And Identification ◽  
Ant Lion

This paper introduces a novel metaheuristic model-based scheme for fault monitoring in squirrel cage induction motors (SCIMs). This method relies on the combination of the ant lion optimizer (ALO) and the unscented Kalman filter (UKF) to detect and quantify the number of broken bars. Contrary to the UKF-based fault diagnosis, the improved ALO-UKF algorithm tunes optimally and automatically the noise covariance matrices Q and R, which reduces the estimation errors, and then obtains an effective and accurate fault diagnosis. Firstly, a mathematical model of the fault under study has been developed based on rotor parameter value as signature. Secondly, a sixth order ALO-UKF algorithm has been synthesized for simultaneous estimation of rotor resistance and speed. Several broken bar fault conditions have been simulated. Simulation results show the effectiveness and robustness of the proposed ALO-UKF scheme in broken bar detection and identification, and exhibit a more superior performance than the simple-UKF and EKF algorithms in term of stability, accuracy and response time.

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