• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1853-1857
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• 2005

In most of motor-driven motion control systems, an encoder is used to measure a position of the motor and the velocity information is obtained by measuring the position increment over a sampling period. The quantization effect due to limited resolution of the encoder induces some measurement errors, and consequently causes deterioration of the motion performance especially in low velocity. In this paper, we propose a gain-scheduled acceleration estimator which works in wider velocity range than the original acceleration estimator. We investigate and analyze characteristics of the velocity measurement mechanism which takes into account the quantization effect of the encoder. Next, we introduce the acceleration estimator and propose a gain-scheduled acceleration estimator. The bandwidth of the gain-scheduled acceleration estimator is automatically adjusted by the velocity command. Finally, its performance is evaluated by simulation and experiment, and the results are compared with those of a conventional method and the original acceleration estimator.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.133-138
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• 1993

This paper presents neural network based position estimation method in slippery environment as an approach to solve one of problems which are engaged in dead reckoning method. Position estimator is composed of slip detector and linear velocity estimator. Both of them are based on the fact that dynamic characteristic of mobile robot in slippery environment is different from the case without slip. To find out the dynamic relation among driving torque, angular acceleration of driving wheel and linear acceleration of mobile robot, accelerometer is used for measuring acceleration of mobile robot and neural network is used for dynamic system identifier in slippery environment.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.4
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• pp.38-46
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• 2011

This paper presents roll angle estimator which used Kalman filter. Recently, the uses of the ELSD (Electronic Limited Slip Differential) and TVD(Torque Vectoring Differential) for vehicle yaw control are studied in many researches. However the roll angle can be negative effect of ELSD and TVD control. Therefore the information of roll angle can be used for vehicle yaw control. Moreover it can be used for rollover prevent control. Recently, most of the vehicles use lateral acceleration and yaw rate sensor. In this paper, design of Kalman filter which used lateral acceleration and yaw rate information is developed. In this paper, in order to verify the estimator ability, the CarSim and Matlab/Simulink are used.

• Smart Structures and Systems
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• v.17 no.4
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• pp.647-667
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• 2016

In this paper, dynamic displacement is estimated with high accuracy by blending high-sampling rate acceleration data with low-sampling rate displacement measurement using a two-stage Kalman estimator. In Stage 1, the two-stage Kalman estimator first approximates dynamic displacement. Then, the estimator in Stage 2 estimates a bias with high accuracy and refines the displacement estimate from Stage 1. In the previous Kalman filter based displacement techniques, the estimation accuracy can deteriorate due to (1) the discontinuities produced when the estimate is adjusted by displacement measurement and (2) slow convergence at the beginning of estimation. To resolve these drawbacks, the previous techniques adopt smoothing techniques, which involve additional future measurements in the estimation. However, the smoothing techniques require more computational time and resources and hamper real-time estimation. The proposed technique addresses the drawbacks of the previous techniques without smoothing. The performance of the proposed technique is verified under various dynamic loading, sampling rate and noise level conditions via a series of numerical simulations and experiments. Its performance is also compared with those of the existing Kalman filter based techniques.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1103-1108
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• 2007

This paper describes a robust model-based roll state estimator for application to the detection of impending vehicle rollover. The roll state estimator is based on a 2-D bicycle model and a roll model to estimate the maneuver-induced vehicle roll motion. The measurement signals are lateral acceleration, yaw rate, steering angle, and vehicle speed. Vehicle mass is adapted to obtain robust performance of the estimator. Computer simulation is conducted to evaluate the proposed roll state estimator by using a validated vehicle simulator. It is shown that the roll state estimator shows robust performance without exact vehicle mass information.

• Smart Structures and Systems
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• v.15 no.2
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• pp.489-503
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• 2015

Response estimation at unmeasured locations using the limited number of measurements is an attractive topic in the field of structural health monitoring (SHM). Because of increasing complexity and size of civil engineering structures, measuring all structural responses from the entire body is intractable for the SHM purpose; the response estimation can be an effective and practical alternative. This paper investigates a response estimation technique based on the Kalman state estimator to combine multi-sensor data under non-zero mean input excitations. The Kalman state estimator, constructed based on the finite element (FE) model of a structure, can efficiently fuse different types of data of acceleration, strain, and tilt responses, minimizing the intrinsic measurement noise. This study focuses on the effects of (a) FE model error and (b) combinations of multi-sensor data on the estimation accuracy in the case of non-zero mean input excitations. The FE model error is purposefully introduced for more realistic performance evaluation of the response estimation using the Kalman state estimator. In addition, four types of measurement combinations are explored in the response estimation: strain only, acceleration only, acceleration and strain, and acceleration and tilt. The performance of the response estimation approach is verified by numerical and experimental tests on a simply-supported beam, showing that it can successfully estimate strain responses at unmeasured locations with the highest performance in the combination of acceleration and tilt.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.2
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• pp.133-140
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• 2008

In this paper, a small and effective attitude estimation system for a humanoid robot was developed. Four small inertial sensors were packed and used for inertial measurements(3D accelerometer and three 1D gyroscopes.) An effective 3D pose estimation algorithm for low cost DSP using an extended Kalman filter was developed and evaluated. The 3D pose estimation algorithm has a very simple structure composed by 3 modules of a linear acceleration estimator, an external acceleration detector and an pseudo-accelerometer output estimator. The algorithm also has an effective switching structure based on probability and simple feedback loop for the extended Kalman filter. A special test equipment using linear motor for the testing of the 3D pose sensor was developed and the experimental results showed its very fast convergence to real values and effective responses. Popular DSP of TMS320F2812 was used to calculate robot's 3D attitude and translated acceleration, and the whole system were packed in a small size for humanoids robots. The output of the 3D sensors(pitch, roll, 3D linear acceleration, and 3D angular rate) can be transmitted to a humanoid robot at 200Hz frequency.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.14-20
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• 2020

This paper presents a vehicle mass and road grade estimator for developing an automated bus. To consider the dynamic characteristics of a bus varying with the number of passengers, the longitudinal controller needs the estimation of the vehicle's mass and road grade in real-time and utilizes the information to adjust the control gains. Discrete Kalman filter is applied to estimate the time-varying road grade, and the recursive least squares algorithm is adopted to account for the constant mass estimation. After being implemented in MATLAB/Simulink, the estimators are evaluated with the dynamic model and experimental data of the target bus. The proposed estimators will be applied to complement the algorithm of the longitudinal controller and proceed with algorithm verification.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.1
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• pp.139-145
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• 1996

The input estimation method estimates maneuvering input acceleration in order to track a maneuvering target. In this paper, the optimal input estimator is derived by choosing the MAP hypothesis among maneuvering input transition hypotheses under the assumption that a maneuvering input acceleration is a semi-Markov process. The optimal input estimation method cannot be realized because the optimal filter should consider every maneuver onset time hypothesis from filter starting time to current time which increase rapidly. Hence the suboptimal filter using a sliding window is proposed. Since the proposed method can consider all hypotheses of input transitions inside the window, it is general enough to include Bogler's input estimation method. Simulation results show, however, that we can obtain a good performance even when the filter considering just one input transition in the window is used. (author). 9 refs., 3 figs., 1 tab.

• Journal of the Earthquake Engineering Society of Korea
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• v.22 no.2
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• pp.77-86
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• 2018

This study develops an empirical prediction equation of spectral acceleration responses of earthquakes which can induce structural damages. Ground motion records representing hazards of low-to-moderate seismic regions were selected and organized with several influential factors affecting the response spectra. The empirical equation and estimator coefficients for acceleration response spectra were then proposed using a robust nonlinear optimization coupled with a regression analysis. For analytical verification of the prediction equation, response spectra used for low-to-moderate seismic regions were estimated and the predicted results were comparatively evaluated with measured response spectra. As a result, the predicted shapes of response spectra can simulate the graphical shapes of measured data with high accuracy and most of predicted results are distributed inside range of correlation of variation (COV) of 30% from perfectly correlated lines.