Traffic Volume Prediction Using Improved Kalman Filter

2014 ◽  
Vol 602-605 ◽  
pp. 3881-3885 ◽  
Author(s):  
Xin Jie Chen ◽  
Ji Hui Ma ◽  
Wei Guan ◽  
Wen Yuan Tu
Keyword(s):  
Kalman Filter ◽  
Empirical Analysis ◽  
Kalman Filtering ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Historical Data ◽  
Traffic Volume ◽  
The State ◽  
State Variable ◽  
Interesting Topic

Traffic volume prediction has been an interesting topic for decades during which various prediction models have been proposed. In this paper, Kalman filtering (KF) model is applied to predict traffic volume because of its significance in continuously updating the state variable as new observations. In order to enhance the prediction accuracy, an improved KF model is developed based on the current and historical data. To validate the improved KF model, empirical analysis is conducted. The results show that the improved KF model has higher accuracy than the traditional one and is more reliable and powerful in traffic volume prediction.

Application of the Extended Kalman Filter to Control of a Shape Memory Alloy Arm

2003 ◽  
Author(s):  
Mohammad H. Elahinia ◽  
Hashem Ashrafiuon ◽  
Mehdi Ahmadian ◽  
Daniel J. Inman
Keyword(s):  
Kalman Filter ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Extended Kalman Filter ◽  
State Vector ◽  
Angular Position ◽  
The State ◽  
State Variables ◽  
Time Step ◽  
State Variable

This paper presents a robust nonlinear control that uses a state variable estimator for control of a single degree of freedom rotary manipulator actuated by Shape Memory Alloy (SMA) wire. A model for SMA actuated manipulator is presented. The model includes nonlinear dynamics of the manipulator, a constitutive model of the Shape Memory Alloy, and the electrical and heat transfer behavior of SMA wire. The current experimental setup allows for the measurement of only one state variable which is the angular position of the arm. Due to measurement difficulties, the other three state variables, arm angular velocity and SMA wire stress and temperature, cannot be directly measured. A model-based state estimator that works with noisy measurements is presented based on the Extended Kalman Filter (EKF). This estimator predicts the state vector at each time step and corrects its prediction based on the angular position measurements. The estimator is then used in a nonlinear and robust control algorithm based on Variable Structure Control (VSC). The VSC algorithm is a control gain switching technique based on the arm angular position (and velocity) feedback and EKF estimated SMA wire stress and temperature. The state vector estimates help reduce or avoid the undesirable and inefficient overshoot problem in SMA one-way actuation control.

scholarly journals Scalable Sparse Testing Genomic Selection Strategy for Early Yield Testing Stage

2021 ◽  
Vol 12 ◽  
Author(s):  
Sikiru Adeniyi Atanda ◽  
Michael Olsen ◽  
Jose Crossa ◽  
Juan Burgueño ◽  
Renaud Rincent ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Prediction Models ◽  
Historical Data ◽  
Coefficient Of Determination ◽  
Selection Strategy ◽  
Environment Interaction ◽  
Genetic Merit ◽  
Testing Stage

To enable a scalable sparse testing genomic selection (GS) strategy at preliminary yield trials in the CIMMYT maize breeding program, optimal approaches to incorporate genotype by environment interaction (GEI) in genomic prediction models are explored. Two cross-validation schemes were evaluated: CV1, predicting the genetic merit of new bi-parental populations that have been evaluated in some environments and not others, and CV2, predicting the genetic merit of half of a bi-parental population that has been phenotyped in some environments and not others using the coefficient of determination (CDmean) to determine optimized subsets of a full-sib family to be evaluated in each environment. We report similar prediction accuracies in CV1 and CV2, however, CV2 has an intuitive appeal in that all bi-parental populations have representation across environments, allowing efficient use of information across environments. It is also ideal for building robust historical data because all individuals of a full-sib family have phenotypic data, albeit in different environments. Results show that grouping of environments according to similar growing/management conditions improved prediction accuracy and reduced computational requirements, providing a scalable, parsimonious approach to multi-environmental trials and GS in early testing stages. We further demonstrate that complementing the full-sib calibration set with optimized historical data results in improved prediction accuracy for the cross-validation schemes.

scholarly journals Forecasting the Cumulative Confirmed Cases with the FGM and Fractional-Order Buffer Operator in Different Stages of COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yanhui Chen ◽  
Minglei Zhang ◽  
Kai Lisa Lo ◽  
Jackson Jinhong Mi
Keyword(s):  
Growth Rate ◽  
Empirical Analysis ◽  
Fractional Order ◽  
Prediction Accuracy ◽  
Historical Data ◽  
Grey Model ◽  
Prevention Measures ◽  
Early Stages ◽  
The Empirical Analysis ◽  
Epidemic Diseases

This study proposes to use the fractional-order accumulation grey model (FGM) combined with the fractional-order buffer operator to predict the cumulative confirmed cases in different stages of COVID-19. In the early stages of COVID-19 outbreak, when the cumulative confirmed cases increased rapidly, we used the strengthening buffer operator in the prediction process. After the government’s prevention measures started to take effect, the growth rate of cumulative confirmed cases slows down. Therefore, the weakening buffer operator is applied in the prediction process. The fractional order of the buffer operator is derived from the historical data, which are more relevant. The empirical analysis of seven countries’ data shows that the FGM with the fractional-order buffer operator achieves the best results for most cases. The fractional-order buffer operator improves the prediction accuracy of the FGM in this study. Our study also suggests a practical way for predicting the trend of epidemic diseases.

Measurement of Step Responses of Flowrate in a Pipe Using a Kalman-Filtering Laminar Flowmeter

2021 ◽  
Author(s):  
Kazushi Sanada
Keyword(s):  
Kalman Filter ◽  
Laminar Flow ◽  
Response Time ◽  
Kalman Filtering ◽  
The State ◽  
Step Response ◽  
Working Fluid ◽  
State Variables ◽  
Turbulent Friction ◽  
Experimental Calibration

Abstract The aim of our research project is to develop a Kalman filter system which estimates unsteady flowrate of a pipe using a laminar flowmeter. In this study, incompressible flow is assumed as working fluid. When the flow becomes turbulent, it is difficult to establish flow model for turbulent friction. In this study, a laminar flowmeter is constructed in which thirty-two narrow pipes of 1mm in inner diameter are bundled and inserted in main flow path. When fluid flow in the narrow pipe is laminar flow, the Kalman filter theory can be applied to the flow of the narrow pipe. Kalman filter is applied to one of narrow pipes of laminar flowmeter. Both upstream and downstream pressure signals of the targeted narrow pipe are input to the Kalman filter. Midpoint pressure measured by a pressure sensor is compared with midpoint pressure signal which is estimated by the Kalman filter. When flow is laminar flow or the system has linear characteristics, an error signal between estimated pressure and measured pressure decreases according to Kalman filter principle. As a result, because the state variables of the Kalman filter converge to real variables, unsteady flowrate is estimated from the state variables of the Kalman filter. Experimental calibration of the Kalman-filtering laminar flowmeter under steady-state flow condition has been performed. In this study, experiments of step response of flowrate in a pipe are conducted by constructing an experimental circuit using solenoid valves. The purpose of experiment is confirmation of a response time of the Kalman-filtering laminar flowmeter. As a result of experiments, it was shown that the response time is 0.05s.

Position Sensorless Control Based on Augmented Extended Kalman Filter for Permanent Magnet Linear Synchronous Motor

2011 ◽  
Vol 88-89 ◽  
pp. 350-354
Author(s):  
Hua Cai Lu ◽  
Ming Jiang ◽  
Li Sheng Wei ◽  
Bing You Liu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Sensorless Control ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
The State ◽  
State Variable ◽  
Detailed Explanation ◽  
System States ◽  
Position Sensorless

In order to achieve position sensorless control for PMLSM drive system, speed and position of the motor must be estimated. A novel sensorless position and speed estimation algorithm was designed for PMLSM drive by measuring terminal voltages and currents. That was state augmented extended Kalman filter (AEKF) estimation method. The resistance of the motor was augmented to the state variable. Then, the speed, position and the resistance were estimated simultaneously through extended Kalman filter (EKF). The influence of the resistance on the state estimation results could be reduced. As well as giving a detailed explanation of the new algorithm, experimental results were presented. It shows that the AEKF is capable of estimating system states accurately and reliability, and the proposed sensorless control system has a good dynamic response.

scholarly journals Aircraft Turbofan Engine Health Estimation Using Constrained Kalman Filtering

2003 ◽  
Author(s):  
Dan Simon ◽  
Donald L. Simon
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Kalman Filters ◽  
Inequality Constraints ◽  
Computational Effort ◽  
Estimation Accuracy ◽  
State Variables ◽  
Turbofan Engine ◽  
State Variable ◽  
State Variable Constraints

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This paper develops an analytic method of incorporating state variable inequality constraints in the Kalman filter. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The incorporation of state variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is proven theoretically and shown via simulation results obtained from application to a turbofan engine model. This model contains 16 state variables, 12 measurements, and 8 component health parameters. It is shown that the new algorithms provide improved performance in this example over unconstrained Kalman filtering.

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.

scholarly journals A Nonlinear Rank Regression Method for Ensemble Kalman Filter Data Assimilation

2019 ◽  
Vol 147 (8) ◽  
pp. 2847-2860 ◽  
Author(s):  
Jeffrey L. Anderson
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
The State ◽  
Single Observation ◽  
Rank Regression ◽  
State Variable ◽  
Rank Histogram ◽  
Observation Space ◽  
The Impact ◽  
Standard Regression

Abstract It is possible to describe many variants of ensemble Kalman filters without loss of generality as the impact of a single observation on a single state variable. For most ensemble algorithms commonly applied to Earth system models, the computation of increments for the observation variable ensemble can be treated as a separate step from computing increments for the state variable ensemble. The state variable increments are normally computed from the observation increments by linear regression using the prior bivariate ensemble of the state and observation variable. Here, a new method that replaces the standard regression with a regression using the bivariate rank statistics is described. This rank regression is expected to be most effective when the relation between a state variable and an observation is nonlinear. The performance of standard versus rank regression is compared for both linear and nonlinear forward operators (also known as observation operators) using a low-order model. Rank regression in combination with a rank histogram filter in observation space produces better analyses than standard regression for cases with nonlinear forward operators and relatively large analysis error. Standard regression, in combination with either a rank histogram filter or an ensemble Kalman filter in observation space, produces the best results in other situations.

scholarly journals Aircraft Turbofan Engine Health Estimation Using Constrained Kalman Filtering

2005 ◽  
Vol 127 (2) ◽  
pp. 323-328 ◽  
Author(s):  
Dan Simon ◽  
Donald L. Simon
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Kalman Filters ◽  
Inequality Constraints ◽  
Computational Effort ◽  
Estimation Accuracy ◽  
State Variables ◽  
Turbofan Engine ◽  
State Variable ◽  
State Variable Constraints

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state-variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This paper develops an analytic method of incorporating state-variable inequality constraints in the Kalman filter. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The incorporation of state-variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is proven theoretically and shown via simulation results obtained from application to a turbofan engine model. This model contains 16 state variables, 12 measurements, and 8 component health parameters. It is shown that the new algorithms provide improved performance in this example over unconstrained Kalman filtering.

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