Calibration using adaptive model complexity for parallel and fiber-driven mechanisms

Robotica ◽  
2015 ◽  
Vol 34 (6) ◽  
pp. 1416-1435
Author(s):  
Tomáš Skopec ◽  
Zbyněk Šika ◽  
Michael Valášek
Keyword(s):  
Linear Models ◽  
Calibration Method ◽  
Model Complexity ◽  
Parallel Mechanisms ◽  
Nonlinear Properties ◽  
Adaptive Calibration ◽  
Model Variant ◽  
Joint Shape ◽  
Kinematic Joint ◽  
Calibration Step

SUMMARYThe paper deals with the development and application of a new adaptive calibration method that extends the geometrical calibration of mechanisms from calibration of only dimensions and kinematical joint positions into the calibration of kinematic joint shape imperfections. Originally unknown nonlinear properties of the kinematical pairs are adaptively included into the kinematical models that are used during the calibration calculation. The method uses description by local linear models and validity functions in order to identify and describe nonlinear properties of the kinematical pairs. During each calibration step, various models with growing complexity are considered before the best model variant is selected to improve calibration results. The method is mainly devoted to the structures with many loops like parallel and fiber-driven parallel mechanisms. The method is applied to parallel mechanism Sliding Star and parallel fiber-driven mechanism Quadrosphere.

scholarly journals Calibration Method for a Parallel Mechanism Type Machine Tool by Response Surface Methodology –Consideration via Simulation on a Stewart Platform Mechanism–

2010 ◽  
Vol 4 (4) ◽  
pp. 355-363 ◽  
Author(s):  
Hiroshi Yachi ◽  
◽  
Hiroshi Tachiya
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Parallel Mechanism ◽  
Measurement Data ◽  
Calibration Method ◽  
Stewart Platform ◽  
Parallel Mechanisms ◽  
Data Set ◽  
Efficient Data ◽  
Efficient Measurement

This paper proposes a calibration method for parallel mechanisms usingResponse Surface Methodology. This method is a statistical approach to estimating an unknown input-output relationship using a small set of efficient data collected on an intended system. Although identifying locations causing positional errors in a parallel mechanism and precisely measuring the position and posture of the output point are difficult, the proposed calibration method based onResponse Surface Methodologyaims to compensate for positional and postural errors, without indentifying the locations causing these errors, by using a small yet efficient measurement data set. This study analyzes the effectiveness of the method we propose by applying it to a Stewart platform, which is a typical spatial 6-DOF parallel mechanism.

Real-time self-adaptive calibration method for high speed acquisition system

2019 ◽  
Vol 90 (1) ◽  
pp. 015118 ◽  
Author(s):  
Hao Zeng ◽  
Peng Ye ◽  
Wentao Wei ◽  
Lianping Guo ◽  
Huiqing Pan ◽  
...  
Keyword(s):  
Real Time ◽  
High Speed ◽  
Calibration Method ◽  
Acquisition System ◽  
Adaptive Calibration ◽  
Self Adaptive

scholarly journals Adaptive calibration of the angle sensor for gyroscope

2021 ◽  
pp. 85-88
Author(s):  
V. A. Smirnov ◽  
◽  
A. B. Snedkov ◽  
Keyword(s):  
Flight Control ◽  
Test Bench ◽  
Calibration Method ◽  
Sensor Calibration ◽  
Flight Control System ◽  
Angle Sensor ◽  
Measuring Systems ◽  
Network Training ◽  
Adaptive Calibration ◽  
Methods Of Control

The article proposes methods of control and testing of information-measuring systems. The methods for calibrating the angle sensor included in the flight control system are considered. The principle of creating a test bench for evaluating the accuracy of angle sensor calibration is defined. The process of neural network training is described, which allows to compensate the disadvantages inherent to the traditional calibration method. A comparative analysis of the traditional method of joint calibration of angle sensors with the method based on the use of neural networks is performed

PREDICTABILITY OF MOVING AVERAGE RULES AND NONLINEAR PROPERTIES OF STOCK RETURNS: EVIDENCE FROM THE CHINA STOCK MARKET

2011 ◽  
Vol 07 (02) ◽  
pp. 267-279 ◽  
Author(s):  
Zhigang Wang ◽  
Yong Zeng ◽  
Heping Pan ◽  
Ping Li
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Dynamic Process ◽  
Linear Models ◽  
Moving Average ◽  
Ann Model ◽  
Nonlinear Properties ◽  
China Stock Market ◽  
Artificial Neural Network Ann ◽  
Predictability Of Returns

This paper investigates the predictability of moving average rules for the China stock market. We find that buy signals generate higher returns and less volatility, while returns following sell signals are negative and more volatile. Moreover, the bootstrapping results indicate that the asymmetrical patterns of return and volatility between buy and sell signals cannot be explained by four popular linear models of returns, especially the phenomenon of negative sell returns. We then test the nonlinear dynamic process of returns. Although the existing artificial neural network (ANN) model can replicate the negative sell returns, it fails to capture the volatility patterns of buy and sell returns. Furthermore, we introduce the conditional heteroskedasticity structure into the ANN model and find that the revised ANN model cannot only explain the predictability of returns, but can also capture the patterns of buy and sell volatility, which are never achieved by any linear model of returns tested in the related literature. Therefore, we conclude that the moving average trading rules can pick up some of the hidden nonlinear patterns in the dynamic process of stock returns, which may be the reason why they can be used to predict price changes.

Evaluation of various models for respiratory inductance plethysmography calibration

1993 ◽  
Vol 74 (3) ◽  
pp. 1206-1211 ◽  
Author(s):  
N. O. Stromberg ◽  
G. O. Dahlback ◽  
P. M. Gustafsson
Keyword(s):  
Lung Volume ◽  
Linear Models ◽  
Body Position ◽  
Calibration Method ◽  
Calibration Model ◽  
Linear Calibration ◽  
Volume Changes ◽  
Rib Cage ◽  
Respiratory Phase ◽  
History Of

We evaluated one nonlinear and two linear models of the ventilatory system while calibrating the respiratory inductance plethysmograph (RIP) against a pneumotachometer. A calibration method involving voluntary varying rib cage and abdominal contributions to tidal volume in a single body position was utilized. The influence on accuracy of the choice of respiratory phase during calibration was assessed. Both tidal and intratidal volumes were evaluated. Ten adults with no history of respiratory disorders went through RIP calibration and validation in the sitting and supine positions. A linear calibration model, relating lung volume changes from the start of inspiration or expiration to rib cage and abdominal excursions from initiation of respiratory motion, had the best accuracy. The choice of respiratory phase for calibration did not affect accuracy. RIP generally underestimated lung volume at the start of inspiration and overestimated lung volume at the end of inspiration. RIP was more accurate in the supine than the sitting position, probably because of limited spine flexion in the supine position.

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