Experimental Evaluation of the Preview Servo Scheme for a Two-Axis Positioning System

1984 ◽  
Vol 106 (1) ◽  
pp. 1-5 ◽  
Author(s):  
M. Tomizuka ◽  
D. Dornfeld ◽  
X.-Q. Bian ◽  
H.-G. Cai
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Experimental Evaluation ◽  
Experimental Results ◽  
Positioning System ◽  
Velocity Control ◽  
Filtering Theory ◽  
On Line ◽  
Sharp Corners ◽  
Welding System

A preview servo scheme for position and velocity control is implemented on a two-axis welding table. The Kalman filtering theory is used to estimate the velocity from position measurements, and a cornering scheme is proposed to attain smaller path errors at sharp corners. The experimental results show that the preview-servo scheme with the Kalman filter and corner preview features is suitable for on-line control of the welding system.

Kalman Filtering for Seam Tracking in Master-Slave Robot Remote Welding System

2011 ◽  
Vol 314-316 ◽  
pp. 1005-1008
Author(s):  
Hong Tang Chen ◽  
Hai Chao Li ◽  
Hong Ming Gao ◽  
Lin Wu
Keyword(s):  
Kalman Filtering ◽  
Seam Tracking ◽  
Experimental Results ◽  
Welding Quality ◽  
Welding Seam ◽  
Tracking Process ◽  
Key Factor ◽  
Remote Welding ◽  
Welding System

Welding seam tracking precision is a key factor influencing welding quality for master-slave robot remote welding system. However, it does not satisfy the welding requirement due to significant noises. To eliminate the influence of noises upon the seam tracking precision and improve the seam tracking precision, a master-slave robot remote welding system was built and Kalman filtering (KF) was applied to the seam tracking process. The experimental results show that the KF eliminated the influence of noises upon the seam tracking precision and improved the seam tracking precision.

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.

scholarly journals PREDIKSI CURAH HUJAN BULANAN BERDASARKAN SUHU PERMUKAAN LAUT NINO 3.4 : SUATU PENDEKATAN DENGAN METODE FILTER KALMAN(MONTHLY RAINFALL PREDICTION BASED ON SEA SURFACE TEMPERATURE NINO 3.4 : THE APPROACH WITH KALMAN FILTERING)

Agromet ◽  
2005 ◽  
Vol 19 (2) ◽  
pp. 43
Author(s):  
Woro Estinigtyas ◽  
S. Suciantini ◽  
G. Irianto
Keyword(s):  
Kalman Filter ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Correlation Coefficient ◽  
Kalman Filtering ◽  
Sea Surface ◽  
Deterministic Models ◽  
Rainfall Prediction ◽  
Dependent Variables ◽  
On Line

Many approaches have been applied to forecast climate using statistical and deterministic models using independent and dependent variables empirically. It is more practical to analyze the parameters, but it needs validation anytime and anywhere. Kalman filtering unites physical and statistical model approaches to stochastic model renewable anytime for objective of on line forecasting. Based on research, sea surface temperature Nino 3.4 have high correlation with rainfall in Indonesia, so it is used to forecast rainfall in Cirebon as area study. Rainfall clustering in Cirebon results 6 groups with rainfall average 1400-1500 mm/year for dry area and 3000-3200 mm/year for wet area. Validation have correlation coefficient validation value more than 94%, correlation coefficient model value more than 78% and fit model value more than 38%. The result of regression gives R2 value of more than 0,8. It implies that predicting model using Kalman Filter is feasible to forecast montly rainfall based on sea surface temperature Nino 3.4. The result of rainfall prediction in Cirebon show increasing in rainfall until February 2005, with correlation coeficient value of model more than 90% and fit model more than 40%.

scholarly journals Computing Engine Efficiencies by Neural Nets

1995 ◽  
Author(s):  
T. W. Long
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Estimation Method ◽  
Engine Performance ◽  
Neural Nets ◽  
Neural Network Approach ◽  
Current Estimation ◽  
Filter Output ◽  
On Line ◽  
Good Agreement

The Performance Seeking Control (PSC) system for the Pratt & Whitney 1128 Engine requires estimates of engine performance; however, the current estimation method of Kalman Filtering imposes a heavy computation overhead. Thus fast on-line applications may be limited. A neural network approach to estimating engine performance is investigated as an alternative method. This neuro-estimator emulates the Kalman Filter based on off-line training. Good agreement between Kalman Filter output and the neuro-estimator output was achieved for steady state conditions.

Adaptive Kalman Filtering for Low-cost INS/GPS

2003 ◽  
Vol 56 (1) ◽  
pp. 143-152 ◽  
Author(s):  
Christopher Hide ◽  
Terry Moore ◽  
Martin Smith
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Inertial Sensor ◽  
Low Cost ◽  
Simulation Software ◽  
Multiple Model ◽  
On Line ◽  
Stochastic Properties ◽  
Dynamic Alignment ◽  
Filtering Techniques

GPS and low-cost INS sensors are widely used for positioning and attitude determination applications. Low-cost inertial sensors exhibit large errors that can be compensated using position and velocity updates from GPS. Combining both sensors using a Kalman filter provides high-accuracy, real-time navigation. A conventional Kalman filter relies on the correct definition of the measurement and process noise matrices, which are generally defined a priori and remain fixed throughout the processing run. Adaptive Kalman filtering techniques use the residual sequences to adapt the stochastic properties of the filter on line to correspond to the temporal dependence of the errors involved. This paper examines the use of three adaptive filtering techniques. These are artificially scaling the predicted Kalman filter covariance, the Adaptive Kalman Filter and Multiple Model Adaptive Estimation. The algorithms are tested with the GPS and inertial data simulation software. A trajectory taken from a real marine trial is used to test the dynamic alignment of the inertial sensor errors. Results show that on line estimation of the stochastic properties of the inertial system can significantly improve the speed of the dynamic alignment and potentially improve the overall navigation accuracy and integrity.

scholarly journals Examination of Multi-Receiver GPS/EGNOS Positioning with Kalman Filtering and Validation Based on CORS Stations

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2732 ◽  
Author(s):  
Adam Ciećko ◽  
Mieczysław Bakuła ◽  
Grzegorz Grunwald ◽  
Janusz Ćwiklak
Keyword(s):  
Kalman Filter ◽  
Global Positioning System ◽  
Kalman Filtering ◽  
Test Area ◽  
Positioning System ◽  
Final Solution ◽  
Gps Receivers ◽  
Reliable Monitoring ◽  
Global Positioning

This paper presents the concept of precise navigation based on SBAS technology and CORS stations. In a kinematic test, three rover Global Positioning System (GPS) receivers, properly spaced relatively to each other, were used in order to estimate reliable and redundant GPS/EGNOS positions. Next, the Kalman filter was employed to give the final solution. It was proven that EGNOS positioning allows to obtain an accuracy in the range of about 0.5–1.5 m. The proposed solution involving the use of three mobile receivers and Kalman filtering allowed to reduce the 3D error to a level below 0.3 m. Such an accuracy was achieved using only GPS L1 code observations and EGNOS corrections. Additionally, a reliable monitoring of quality of GPS/EGNOS positioning in the test area based on CORS stations was presented.

scholarly journals Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

2009 ◽  
Vol 132 (3) ◽  
Author(s):  
Donald L. Simon ◽  
Sanjay Garg
Keyword(s):  
Kalman Filter ◽  
Estimation Error ◽  
Engine Performance ◽  
Aircraft Engine ◽  
Operating Conditions ◽  
Performance Estimation ◽  
Experimental Simulation ◽  
Tuning Parameter ◽  
Estimation Accuracy ◽  
On Line

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multivariable iterative search routine that seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared with the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy.

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