scholarly journals Iterated extended Kalman filter based state estimation of diode circuit

2021 ◽  
Vol 2070 (1) ◽  
pp. 012092
Author(s):  
Amit Kumar Gautam ◽  
Sudipta Majumdar
Keyword(s):  
Performance Evaluation ◽  
Kalman Filter ◽  
Root Mean Square Error ◽  
State Estimation ◽  
Mean Square Error ◽  
Extended Kalman Filter ◽  
Kalman Filtering ◽  
Mean Square ◽  
Extended Kalman Filtering ◽  
Iterated Extended Kalman Filter

Abstract This paper presents the state estimation of diode circuit using iterated extended Kalman filter (IEKF). The root mean square error (RMSE) based performance evaluation gives the superiority of the IEKF based estimation over extended Kalman filtering (EKF) based method.

Two-layer structure algorithm for estimation of commercial vehicle mass

2019 ◽  
Vol 234 (2-3) ◽  
pp. 378-389
Author(s):  
Liuliu Cai ◽  
Hongliang Wang ◽  
Tianle Jia ◽  
Pai Peng ◽  
Dawei Pi ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Extended Kalman Filter ◽  
Commercial Vehicle ◽  
Layer Structure ◽  
Estimation Algorithm ◽  
Mean Square ◽  
Mass Estimation ◽  
Grade Estimation

Aiming at the problem of mass estimation for commercial vehicle, a two-layer structure mass estimation algorithm was proposed. The first layer was the grade estimation algorithm based on recursive least squares method and the second layer was a mass estimation algorithm using the extended Kalman filter. The estimated grade was introduced as the observation quantity of the second layer. The influence of the suspension deformation on grade estimation was considered in the first layer algorithm, which was corrected in real time according to the mass and road grade estimated by the second layer algorithm. The proposed estimation algorithm was validated via a co-simulation platform involving TruckSim and MATLAB/Simulink. Finally, a road test was carried out, and the evaluation method using the root mean square error was proposed. According to the test, the average value of the root mean square error reduces from 871.65 to 772.52, grade estimation is more accurate, and the convergence speed of mass estimation is faster, compared with estimation results of the extended Kalman filter method.

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.

SYNCHRONIZATION THROUGH FILTERING

2000 ◽  
Vol 10 (04) ◽  
pp. 763-775 ◽  
Author(s):  
C. CRUZ ◽  
H. NIJMEIJER
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Discrete Time ◽  
Kalman Filtering ◽  
Private Communication ◽  
Extended Kalman Filtering ◽  
Extensive Simulation ◽  
Synchronization Problem

We study the synchronization problem in discrete-time via an extended Kalman filter (EKF). That is, synchronization is obtained of transmitter and receiver dynamics in case the receiver is given via an EKF that is driven by a noisy drive signal from a noisy transmitter dynamics. The convergence of the filter dynamics towards the transmitter dynamics is rigorously shown using recent results in extended Kalman filtering. Two extensive simulation examples show that the filter is indeed suitable for synchronization of (noisy) chaotic transmitter dynamics. An application to private communication is also given.

scholarly journals Extended Kalman filter approach to electric arc furnace forecasting

2021 ◽  
Author(s):  
Ali Hosseignholizadeh
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Kalman Filtering ◽  
Past History ◽  
Multivariate Time Series ◽  
Arc Furnace ◽  
Extended Kalman Filtering ◽  
History Of ◽  
Auto Regressive ◽  
Filter Approach

In this thesis, we propose and implement a new approach for building an online self-adjusting model for prediction of v-i characteristic of a multivariate time series obtained from an operational electrical arc furnace. The proposed methodology is based on the Kalman filtering method, and is used for prediction of the arc furnace voltage using the past history of the current and voltage. The main advantage of the proposed approach over similar earlier related work is the ability to adapt during the operation of the furnace. In this study, three different hybrid models have been developed based on the extended Kalman filtering technique and one of the following methodologies: (i) a linar auto regressive model; (ii) fuzzy logic, (iii) wavelet analysis. The results compare well with those of earlier work and clearly indicate that the augmentation of the above mentioned approaches with the extended Kalman filter improves the prediction accuracy.

Slow On-Off Control of Two-State Damping for Electrically Controlled Air Suspension Based on Extended Kalman Filter

2010 ◽  
Vol 39 ◽  
pp. 504-509
Author(s):  
Xing Xu ◽  
Zhong Xin Li ◽  
Qi Yao Yang ◽  
Chao Feng Pan
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Kalman Filtering ◽  
Shock Absorber ◽  
Extended Kalman Filtering ◽  
Air Suspension ◽  
Nonlinear Character ◽  
Opening And Closing ◽  
Vehicle Testing ◽  
Speed Simulation

To improve riding performance of the bus with air suspension, the half-car nonlinear model of air suspension was created with the help of analyzing nonlinear character of spring-pneumatic. Slow on-off control of two-state damping was proposed according to the tire deformation and suspension deflection, and the shock absorber with two-state damping was designed by opening and closing the orifice of throttle. It was difficult to obtain the tire deformation directly, and the state-observer of air suspension was proposed based on Extended Kalman Filtering (EKF) algorithm, namely the tire deformation was estimated by the suspension deflection and its relative changing speed. Simulation shows EKF algorithm can estimate the running states well, and the estimating error of RMS is below 10% in 3 seconds and the whole vehicle testing validate that the slow on-off control can enhance the capability of air suspension.

scholarly journals State Estimation and Forecasting of the Ski-Slope Model Using an Improved Shadowing Filter

2016 ◽  
Vol 26 (04) ◽  
pp. 1650056
Author(s):  
Auni Aslah Mat Daud
Keyword(s):  
Root Mean Square Error ◽  
State Estimation ◽  
Mean Square Error ◽  
Gradient Descent ◽  
Slight Modification ◽  
Mean Square ◽  
Step Size ◽  
Adaptive Step Size ◽  
Adaptive Step

In this paper, we present the application of the gradient descent of indeterminism (GDI) shadowing filter to a chaotic system, that is the ski-slope model. The paper focuses on the quality of the estimated states and their usability for forecasting. One main problem is that the existing GDI shadowing filter fails to provide stability to the convergence of the root mean square error and the last point error of the ski-slope model. Furthermore, there are unexpected cases in which the better state estimates give worse forecasts than the worse state estimates. We investigate these unexpected cases in particular and show how the presence of the humps contributes to them. However, the results show that the GDI shadowing filter can successfully be applied to the ski-slope model with only slight modification, that is, by introducing the adaptive step-size to ensure the convergence of indeterminism. We investigate its advantages over fixed step-size and how it can improve the performance of our shadowing filter.

scholarly journals COMPARISON OF GPS SMOOTHENING METHODS BETWEEN EXTENDED KALMAN FILTER AND PARTICLE FILTER FOR UAV

2017 ◽  
Keyword(s):  
Numerical Simulation ◽  
Kalman Filter ◽  
Particle Filter ◽  
Extended Kalman Filter ◽  
Kalman Filtering ◽  
Particle Filtering ◽  
State Vector ◽  
The State ◽  
Gps Data ◽  
Extended Kalman Filtering

This paper presents a method for smoothing GPS data from a UAV using Extended Kalman filtering and particle filtering for navigation or position control. A key requirement for navigation and control of any autonomous flying or moving robot is availability of a robust attitude estimate. Consider a dynamic system such as a moving robot. The unknown parameters, e.g., the coordinates and the velocity, form the state vector. This time dependent vector may be predicted for any instant time by means of system equations. The predicted values can be improved or updated by observations containing information on some components of the state vector. The whole procedure is known as Kalman filtering. On the other hand, the particle filtering algorithm is to perform a recursive Bayesian filter by Monte Carlo simulations. The key is to represent the required posterior density function by a set of random samples, which is called particles with associated weights, and to compute estimates based on these samples as well as weights. We compare the two GPS smoothening methods: Extended Kalman Filter and Particle Filter for mobile robots applications. Validity of the smoothing methods is verified from the numerical simulation and the experiments. The numerical simulation and experimental results show the good GPS data smoothing performance using Extended Kalman filtering and particle filtering.

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