Extended Kalman filter-based state estimation of MOSFET circuit

2019 ◽  
Vol 38 (6) ◽  
pp. 1885-1903
Author(s):  
Rahul Bansal ◽  
Sudipta Majumdar
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Input Signal ◽  
Output Voltage ◽  
State Space Model ◽  
Oxide Semiconductor ◽  
Content Type ◽  
Saturation Region ◽  
Inaccurate Estimation ◽  
Effect Transistor

Purpose This paper aims to present the estimation of the output voltage of metal oxide semiconductor field effect transistor (MOSFET) using the extended Kalman filter (EKF) method. Design/methodology/approach The method uses EKF for MOSFET output voltage estimation. To implement the EKF method, the state space model has been obtained using Kirchhoff’s current law and Enz-Krummenacher-Vittoz model of the MOSFET circuit. Findings The proposed method can be used for any mode of MOSFET operation besides near the quiescent point region. The nonlinearity that occurs in the saturation region of MOSFET can also be considered in the proposed method. The proposed method can also be used for a large input signal. Though Kalman filter can be used for the small amplitude input signal, it results in inaccurate estimation due to the linearization of the nonlinear system. Research limitations/implications The method is able to track the parameters when they are slowly changing with time. Originality/value The proposed method presents maximal precision of simulation as the maximal precision of simulation requires modeling of the circuit in terms of device parameters and circuit elements.

Sensorless finite-state predictive torque control of induction motor fed by four-switch inverter using extended Kalman filter

2018 ◽  
Vol 37 (6) ◽  
pp. 2006-2024 ◽  
Author(s):  
Mohamed Chebaani ◽  
Amar Goléa ◽  
Med Toufik Benchouia ◽  
Noureddine Goléa
Keyword(s):  
Kalman Filter ◽  
Induction Motor ◽  
Extended Kalman Filter ◽  
Direct Torque Control ◽  
Operating Conditions ◽  
Torque Control ◽  
Switching Frequency ◽  
Content Type ◽  
Finite State ◽  
Torque Ripples

Purpose Direct Torque Control (DTC) of induction motor drives is a well-established technique owing to features such as fast dynamic and insensibility to motor parameters. However, conventional DTC scheme, based on comparators and the switching table, suffers from large torque and flux ripples. To improve DTC performance, this study aims to propose and implement a sensorless finite-state predictive torque control using extended Kalman Filter in dSPACE environment. Design/methodology/approach This paper deals with the design of an extended Kalman filter for estimating the state of an induction motor model and for sensorless control of systems using this type of motor as an actuator. A complex-valued model is adopted that simultaneously allows a simpler observability analysis of the system and a more effective state estimation. Findings Simulation and experimental results reveal that the drive system, associated with this technique, can effectively reduce flux and torque ripples with better dynamic and steady state performance. Further, the proposed approach maintains a constant switching frequency. Originality/value The proposed speed observer have been developed and implemented experimentally under different operating conditions such as parameter variation, no-load/load disturbances and speed variations in different speed operation regions.

Parameter Estimation in a Hydrostatic System Using Extended Kalman Filter

2001 ◽  
Author(s):  
Yuvin A. Chinniah ◽  
Richard Burton ◽  
Saeid Habibi
Keyword(s):  
Kalman Filter ◽  
Bulk Modulus ◽  
Extended Kalman Filter ◽  
High Performance ◽  
State Space Model ◽  
System Response ◽  
Actuation System ◽  
Accuracy And Precision ◽  
Hydrostatic System ◽  
Entrained Air

Abstract In this paper, the Extended Kalman Filter (EKF) estimation technique is applied to a novel hydrostatic actuation system referred to as the Electrohydraulic Actuator (EHA). A state space model of the EHA is developed and the effective bulk modulus is estimated in simulation. The EHA is a high performance actuation system capable of moving large loads with very high accuracy and precision. In a practical situation, this parameter is very difficult to measure directly as it depends on entrained air which cannot be known at a particular point of time. The bulk modulus is critical for system response and a low bulk modulus as a result of air in the system can seriously hinder the performance of EHA and cause safety problems.

Sensor-less control of a novel axial flux-switching permanent-magnet motor

2018 ◽  
Vol 37 (6) ◽  
pp. 2299-2312
Author(s):  
Javad Rahmani Fard ◽  
Mohammad Ardebili
Keyword(s):  
Kalman Filter ◽  
Permanent Magnet ◽  
Extended Kalman Filter ◽  
Current Sensor ◽  
The Novel ◽  
Permanent Magnet Motor ◽  
Axial Flux ◽  
Content Type ◽  
Stator Current ◽  
Current Sensors

Purpose The purpose of this paper is to suggest a novel current sensor-less drive system for a novel axial flux-switching permanent-magnet motor drive to reduce the costs and avoid problems caused by faults of the current sensors. Design/methodology/approach Commonly, a conventional controller needs at least two current sensors; in this paper, the current sensors are removed by replacing estimated stator current with the extended Kalman filter. Findings A prototype of the novel axial flux-switching permanent-magnet motor is fabricated and tested. It is found that the experimental results confirm the proposed method and show that the control has almost the same performance and ability as the conventional control. Originality/value The axial flux-switching permanent-magnet motor is one of the most efficient motors, but current sensor-less control of an axial flux-switching permanent-magnet motor with a sandwiched permanent magnet and a unity displacement winding factor has not been specially reported to date. Thus, in this paper, the authors report on current sensor-less control based on the extended Kalman filter for electric vehicles.

Design of Extended Kalman Filter for a Shape Memory Alloy Manipulator

2003 ◽  
Author(s):  
Mohammad H. Elahinia ◽  
Hashem Ashrafiuon ◽  
Mehdi Ahmadian ◽  
William T. Baumann
Keyword(s):  
Kalman Filter ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Extended Kalman Filter ◽  
State Space Model ◽  
Angular Position ◽  
The State ◽  
State Variables ◽  
Time Step ◽  
Noisy Measurements

This paper presents an Extended Kalman Filter (EKF) for estimation of the state variables of a single degree of freedom rotary manipulator actuated by Shape Memory Alloy (SMA). A state space model for the SMA manipulator is presented. The model includes nonlinear dynamics of the manipulator, constitutive model of Shape Memory Alloy, and the electrical and heat transfer behavior of SMA wire. In the experimental setup, angular position of the arm is the only state variable that is measured. The other state variables of the system are arm’s angular velocity, SMA wire’s stress, temperature and the Martensite factor, which are not available experimentally due to measurement difficulties. Hence, a model-based state estimator that works with noisy measurements is presented based on the Extended Kalman Filter. This estimator predicts the state vector at each time step and corrects its prediction based on the angular position of the arm which can be measured experimentally. The state variables collected through model simulations are also used to evaluate the performance of the EKF. Several EKF simulations are presented that show accurate, and robust performance of the estimator for different types of inputs.

Magnetometer-based autonomous orbit determination via a measurement differencing extended Kalman filter during geomagnetic storms

2020 ◽  
Vol 92 (3) ◽  
pp. 428-439
Author(s):  
Feng Cui ◽  
Dong Gao ◽  
Jianhua Zheng
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Orbit Determination ◽  
Geomagnetic Field ◽  
Field Model ◽  
Geomagnetic Storms ◽  
Content Type ◽  
Geomagnetic Field Model ◽  
Autonomous Orbit Determination ◽  
Time Accuracy

Purpose The main reason for the low accuracy of magnetometer-based autonomous orbit determination is the coarse accuracy of the geomagnetic field model. Furthermore, the geomagnetic field model error increases obviously during geomagnetic storms, which can still further reduce the navigation accuracy. The purpose of this paper is to improve the accuracy of magnetometer-based autonomous orbit determination during geomagnetic storms. Design/methodology/approach In this paper, magnetometer-based autonomous orbit determination via a measurement differencing extended Kalman filter (MDEKF) is studied. The MDEKF algorithm can effectively remove the time-correlated portion of the measurement error and thus can evidently improve the accuracy of magnetometer-based autonomous orbit determination during geomagnetic storms. Real flight data from Swarm A are used to evaluate the performance of the MDEKF algorithm presented in this study. A performance comparison between the MDEKF algorithm and an extended Kalman filter (EKF) algorithm is investigated for different geomagnetic storms and sampling intervals. Findings The simulation results show that the MDEKF algorithm is superior to the EKF algorithm in terms of estimation accuracy and stability with a short sampling interval during geomagnetic storms. In addition, as the size of the geomagnetic storm increases, the advantages of the MDEKF algorithm over the EKF algorithm become more obvious. Originality/value The algorithm in this paper can improve the real-time accuracy of magnetometer-based autonomous orbit determination during geomagnetic storms with a low computational burden and is very suitable for low-orbit micro- and nano-satellites.

Discussion of the Performance of VRB Based on Modeling by Using EKF to Estimate SOC

2012 ◽  
Vol 512-515 ◽  
pp. 1364-1370 ◽  
Author(s):  
Jin Ding Lu ◽  
Gang Sun ◽  
Yue Jin Tang ◽  
Li Ren ◽  
Jing Shi
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Output Voltage ◽  
Redox Flow Batteries ◽  
Battery Capacity ◽  
Flow Batteries ◽  
Operating Current ◽  
Vanadium Redox ◽  
Capacity Output

There are several ongoing researchers on searching for an appropriate model to describe the characteristic of Vanadium redox flow batteries(VRB) .Based on one of these models, a SOC estimator of VRB- Extended Kalman Filter (EKF) is advanced. And then, update the VRB model by using EKF to estimate SOC when simulation in SIMULINK. At last, the effects of the temperature and operating current on performance of VRB, including battery capacity, output voltage, efficiencies are discussed.

scholarly journals A Progressive Extended Kalman Filter Method for Freeway Traffic State Estimation Integrating Multisource Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yingshun Liu ◽  
Shanglu He ◽  
Bin Ran ◽  
Yang Cheng
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
State Space Model ◽  
Heterogeneous Data ◽  
Filter Method ◽  
Multiple Sources ◽  
Freeway Traffic ◽  
Traffic Conditions ◽  
Kalman Filter Method

Variable techniques have been used to collect traffic data and estimate traffic conditions. In most cases, more than one technology is available. A legitimate need for research and application is how to use the heterogeneous data from multiple sources and provide reliable and consistent results. This paper aims to integrate the traffic features extracted from the wireless communication records and the measurements from the microwave sensors for the state estimation. A state-space model and a Progressive Extended Kalman Filter (PEKF) method are proposed. The results from the field test exhibit that the proposed method efficiently fuses the heterogeneous multisource data and adaptively tracks the variation of traffic conditions. The proposed method is satisfactory and promising for future development and implementation.

Bayesian monthly index for building activity based on mixed frequencies: the case of Chile

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Byron J. Idrovo-Aguirre ◽  
Javier E. Contreras-Reyes
Keyword(s):  
Kalman Filter ◽  
Bayesian Inference ◽  
State Space ◽  
State Space Model ◽  
Natural Extension ◽  
The State ◽  
Likelihood Method ◽  
Content Type ◽  
Space Model ◽  
Construction Activity

PurposeThis paper combines the objective information of six mixed-frequency partial-activity indicators with assumptions or beliefs (called priors) regarding the distribution of the parameters that approximate the state of the construction activity cycle. Thus, this paper uses Bayesian inference with Gibbs simulations and the Kalman filter to estimate the parameters of the state-space model, used to design the Imacon.Design/methodology/approachUnlike other economic sectors of similar importance in aggregate gross domestic product, such as mining and industry, the construction sector lacked a short-term measure that helps to identify its most recent performance.FindingsIndeed, because these priors are susceptible to changes, they provide flexibility to the original Imacon model, allowing for the assessment of risk scenarios and adaption to the greater relative volatility that characterizes the sector's activity.Originality/valueThe classic maximum likelihood method of estimating the monthly construction activity index (Imacon) is rigid to the incorporation of new measures of uncertainty, expectations or different volatility (risks) levels in the state of construction activity. In this context, this paper uses Bayesian inference with 10,000 Gibbs simulations and the Kalman filter to estimate the parameters of the state-space model, used to design the Imacon, inspired by the original works of Mariano and Murasawa (2003) and Kim and Nelson (1998). Thus, this paper consists of a natural extension of the classic method used by Tejada (2006) in the estimation of the old Imacon.

Design of parallel adaptive extended Kalman filter for online estimation of noise covariance

2018 ◽  
Vol 91 (1) ◽  
pp. 112-123 ◽  
Author(s):  
Kai Xiong ◽  
Liangdong Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Position Estimation ◽  
Measurement Noise ◽  
Estimation Accuracy ◽  
Final State ◽  
Content Type ◽  
Noise Covariance ◽  
Adaptive Extended Kalman Filter ◽  
Noise Statistics

Purpose The successful use of the standard extended Kalman filter (EKF) is restricted by the requirement on the statistics information of the measurement noise. The covariance of the measurement noise may deviate from its nominal value in practical environment, and the filtering performance may decline because of the statistical uncertainty. Although the adaptive EKF (AEKF) is available for recursive covariance estimation, it is often less accurate than the EKF with accurate noise statistics. Design/methodology/approach Aiming at this problem, this paper develops a parallel adaptive EKF (PAEKF) by combining the EKF and the AEKF with an adaptive law, such that the final state estimate is dominated by the EKF when the prior noise covariance is accurate, while the AEKF is activated when the actual noise covariance deviates from its nominal value. Findings The PAEKF can reduce the sensitivity of the algorithm to the model uncertainty and ensure the estimation accuracy in the normal case. The simulation results demonstrate that the PAEKF has the advantage of both the AEKF and the EKF. Practical implications The presented algorithm is applicable for spacecraft relative attitude and position estimation. Originality/value The PAEKF is presented for a kind of nonlinear uncertain systems. Stability analysis is provided to show that the error of the estimator is bounded under certain assumptions.

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