• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.1 s.232
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• pp.115-123
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• 2005

This paper presents the development of an algorithm for position control of feed drives in machine tools. The algorithm is constructed through an experimental method based on proportional control with a ramp input. In the first step of designing, a tracking-error curve is generated with the proportional control, and then an error model is decided to reduce the tracking error, Next, the output signal of the error model is added to the current error signal to yield the actuating error signal. The effectiveness of the proposed scheme is confirmed through simulation and experiments.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1144-1148
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• 1996

This paper presents WMR modelling and path tracking algorithm using Inertial Navigation System. The error models of gyroscope and accelerometers in INS are derived by Gauss-Newton method which is nonlinear regression model. Then, to test availability of error model, we pursue the fitness diagnosis about probability characteristic for real data and estimated data. Performance of inertial sensor with error model and Kalman filter is pursued by comparing with one without them. The computer simulation shows that position error remarkably decrease when error compensation is applied.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.8 s.251
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• pp.923-930
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• 2006

A Kriging model is a sort of approximation model and used as a deterministic model of a computationally expensive analysis or simulation. Although it has various advantages, it is difficult to assess the accuracy of the approximated model. It is generally known that a mean square error (MSE) obtained from the kriging model can't calculate statistically exact error bounds contrary to a response surface method, and a cross validation is mainly used. But the cross validation also has many uncertainties. Moreover, the cross validation can't be used when a maximum error is required in the given region. For solving this problem, we first proposed a modified mean square error which can consider relative errors. Using the modified mean square error, we developed the strategy of adding a new sample to the place that the MSE has the maximum when the MSE is used for the assessment of the kriging model. Finally, we offer guidelines for the use of the MSE which is obtained from the kriging model. Four test problems show that the proposed strategy is a proper method which can assess the accuracy of the kriging model. Based on the results of four test problems, a convergence coefficient of 0.01 is recommended for an exact function approximation.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.5
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• pp.584-591
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• 2014

This paper presents a novel aided navigation method for AUV (Autonomous Underwater Vehicles). The navigation system for AUV includes several sensors such as IMU (Inertial Measurement Unit), DVL (Doppler Velocity Log) and depth sensor. In general, the $13^{th}$ order INS error model, which includes depth error, velocity error, attitude error, and the accelerometer and gyroscope biases as state variables is used with measurements from DVL and depth sensors. However, the model may degrade the estimation performance of the heading state. Therefore, the $11^{th}$ INS error model is proposed. Its validity is verified by using a degree of observability and analyzing steady state error. The performance of the proposed model is shown by the computer simulation. The results show that the performance of the reduced $11^{th}$ order error model is better than that of the conventional $13^{th}$ order error model.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.428-433
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• 1999

The errors of IMU(Inertial Measurement Unit) can be divided into deterministic and random errors. Since the required accuracy of the IMU is very high, the errors must be compensated by using an accurate error mode. In this paper, we present a method to get a more accurate error model in a laboratory test. This was done by considering the setting misalignment in the laboratory test in the IMU error model. We considered here the IMU which consits of DTG(dynamically tuned gyroscope) and pendulum type accelerometer. First, it was shown that the estimation result from the model which does not contain the setting misalignment gives considerable estimation error at the validation test. Second, a new model considering the setting misalignment was derived. Finally, by validation test using the estimation results from new model the validity of it was proved.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.3
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• pp.143-151
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• 2001

Machine tool errors have to be characterized and predicted to improve machine tool accuracy. A real-time error compensation system has been developed based on volumetric error synthesis model which is composed of machine tool errors. This paper deals with new algorithm about verification of machine tool errors. This new algorithm uses a simplified volumetric error synthesis model. This simplified model is constructed with only main components among the error components of the machine. The main error components are analyzed by ball bar test of hemispherical helix. The novel measurement method using ball bar system has many advantages which are more efficient, easier to use than conventional measurement system.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.4
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• pp.435-445
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• 2019

Ball screw drives are widely used in industry, and many studies have been devoted on precise, fast and robust control of ball screw drives. In this study, a novel position control algorithm for ball screw drives is proposed, which consist of a PD controller, a friction feedforward and a disturbance observer. The dynamics and the position error of such controller are analyzed to establish an error model, which can be used to predict the resulting position error of the given desired trajectory. Using the proposed error model, the desired trajectory can be modified so that the predicted position error can be compensated in a feedforward manner. The proposed algorithm does not require the model of the system for the error prediction, and thus can be easily applied to conventional control systems. The performance of the system is verified through simulations and experiments.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.258-258
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• 2018

In this study, a hydraulic flow model and an error correction model are combined to improve the flood simulation accuracy. First, the hydraulic flow model is calibrated by optimizing the Manning's roughness coefficient that considers spatial and temporal variability. Then, an error correction model were used to correct the systematic errors of the calibrated hydraulic model. The error correction model is developed using Artificial Neural Networks (ANNs) that can estimate the systematic simulation errors of the hydraulic model by considering some state variables as inputs. The input variables are selected using parital mutual information (PMI) technique. It was found that the calibrated hydraulic model can simulate flood water levels with good accuracy. Then, the accuracy of estimated flood levels is improved further by using the error correction model. The method proposed in this study can be used to the flood control and water resources management as it can provide accurate water level eatimation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.4
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• pp.907-914
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• 1999

This paper studies on the model identification of electro-hydraulic servo systems, which are composed of servo valves, double-rod cylinder and load mass. The identified plant is described as a discrete-time ARX or ARMAX model which is respectively obtained from the identification algorithms of least square error method, instrumental variable method and prediction error method. where a nominal model and the variation of model parameters are quantitatively evaluated.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.3 s.168
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• pp.52-60
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• 2005

This paper deals with the position control fur a feed drive system in CNC milling machine, which utilizes a modified error signal for the elimination of steady-state error. A linear time-invariant (LTI) system has consistent properties in response to standard test signal inputs. Those also appear in an error curve acquired from the response. From such properties, constructed is an error model for the position control of the feed drive. And then added is the output of the error model to the current error signal. Consequently the resulting proportional control system brings performance improvement in view of the steady-state error. The effectiveness of the proposed scheme is confirmed through simulations and experiments.