• Journal of Institute of Control, Robotics and Systems
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• v.15 no.8
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• pp.823-830
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• 2009

For developing automotive parts and telematics devices the real car test often shows limitation because it needs high cost, much time and has the possibility of the accident. Therefore, a Vehicle Driving Simulator (VDS) instead of the real-car test has been used by some automotive manufactures, research centers, and universities. The VDS is a virtual reality device which makes a human being feel as if one drives a vehicle actually. Unlike actual vehicle, the simulator has limited kinematic workspace and bounded dynamic characteristics. So it is difficult to simulate dynamic motions of a multi-body vehicle model fully. In order to overcome these problems, a washout algorithm which restricts workspace of the simulator within the kinematic limits is needed, and analysis of dynamic characteristics is required also. However, a classical washout algorithm contains several problems such as time delay and generation of wrong motion signal caused by characteristics of filters. Specially, the classical washout algorithm has the simulator sickness when driver hardly turns brakes and accelerates the VDS. In this paper, a new washout algorithm is developed to enhance the motion sensitivity and improve the simulator sickness by using the vehicle tilt signal which is generated in the real time vehicle dynamic model.

• Journal of Ocean Engineering and Technology
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• v.2 no.2
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• pp.37-45
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• 1988

A dynamically positioned vessel must be capable of maintaining a specified position and direction by controlling the thruster devices. The motions of a vessel are often assuned to tne sum of low frequency(LF)motions and high frequency(HF)motions. The former is mainly due to wind, current and second order wave forces, while the latter is mainly due to first order wave forces. In order to avoid the high frequency thruser modulation, the control system must include filters to estimate the low frequency motions from the measured motion signals, This paper presents a control system based on Kalman filtering technique and optimal control tyeory. Using the combined kalmam filter, LF motion estimates and HF ones are achieved from the motion measurement of the vessel. The estimated low frequency motions are used as inputs to the dynamic positioning system. The thruster modulation is minimized using the optimal control theory; Linear Quadratic Gaussian(LQG)controller. The performances of the Kalman filter and the dynamic positioned vessel are investigated by computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.362-367
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• 1996

In this paper, the eigenstructure of a class of linear time varying systems, termed as linear quasi-time invariant(LQTI) systems, is investigated. A system composed of dynamic devices such as linear time varying capacitors and resistors can be an example of the class. To effectively describe and analyze the LQTI systems, a generalized differential operator G is introduced. Then the dynamic systems described by the operator G are studied in terms of eigenvalue, frequency characteristics, stability and an extended convolution. Some basic attributes of the operator G are compared with those of the differential operator D. Also the corresponding generalized Laplace transform pair is defined and relevant properties are derived for frequency domain analysis of the systems under consideration. As an application example, a LQTI circuit is examined by using the concept of eigenstructure of LQTI system. The LQTI filter processes the sinusoidal signals modulated by some functions.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.12
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• pp.1306-1316
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• 1990

A controller using a multilayer neural network is proposed to the dynamic control of a PUMA 560 robot arm. This controller is developed based on an error back-propagation (BP) neural network. Since the neural network can model an arbitrary nonlinear mapping, it is used as a commanded feedforward torque generator. A Proportional Derivative (PD) feedback controller is used in parallel with the feedforward neural network to train the system. The neural network was trained by the current state of the manipulator as well as the PD feedback error torque. No a priori knowledge on system dynamics is needed and this information is rather implicitly stored in the interconnection weights of the neural network. In another experiment, the neural network was trained with the current, past and future positions only without any use of velocity sensors. Form this thim window of position values, BP network implicitly filters out the velocity and acceleration components for each joint. Computer simulation demonstrates such powerful characteristics of the neurocontroller as adaptation to changing environments, robustness to sensor noise, and continuous performance improvement with self-learning.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.4
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• pp.203-209
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• 2002

This paper presents a new control scheme for a DVR(Dynamic Voltage Restorer) system consisting of series voltage source PWM converters. To control negative sequence component of source voltage the detection of negative sequence is necessary. Generally, filtering process is used tn do that. Through this filtering process has some problems. This paper suggests a new method of separating positive and negative sequences. This control system is designed using differential controllers and digital filters, and positive sequence and negative sequences are controlled respectively. The performance of the presented controller and scheme are confirmed through simulation and actual experiment by 2.5kVA prototype DVR.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.178.4-178
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• 2001

In this paper a self-tuning gain-scheduled skyhook control for semi-active suspension systems is investigated. The dynamic characteristics of a continuously variable damper including electro-hydraulic pressure control valves is analyzed. A 2-d.o.f. time-varying quarter-car model that permits variations in sprung mass and suspension spring coefficient is considered. The self-tuning skyhook control algorithm proposed in this paper requires only the measurement of body acceleration. The absolute velocity of the sprung mass and the relative velocity of the suspension deflection are estimated by using integral filters. The skyhook gains are gain-scheduled in such a way that the body acceleration and the dynamic tire force are optimized. An ECU prototype ...

• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.137-142
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• 2005

Biomedical signals such as ECG, EMG, and EEG are extremely important in the diagnosis of patients. It is difficult to filter noise from these signals, and errors resulting from filtering can distort a biomedical signal. Existing systems have shown poor performance when complicated noise appears. Adaptive filtering is selected to contend with these defects. Existing adaptive filters can adjust the filter coefficient with the given filter order, but they can produce an unsuitable order in different environments. In order to solve this problem, an optimal adaptive filter with a dynamic structure was designed. Positive experimental results were obtained.

• Earthquakes and Structures
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• v.5 no.3
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• pp.359-378
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• 2013

The problem of identification of multi-component and (or) spatially varying earthquake support motions based on measured responses in instrumented structures is considered. The governing equations of motion are cast in the state space form and a time domain solution to the input identification problem is developed based on the Kalman and particle filtering methods. The method allows for noise in measured responses, imperfections in mathematical model for the structure, and possible nonlinear behavior of the structure. The unknown support motions are treated as hypothetical additional system states and a prior model for these motions are taken to be given in terms of white noise processes. For linear systems, the solution is developed within the Kalman filtering framework while, for nonlinear systems, the Monte Carlo simulation based particle filtering tools are employed. In the latter case, the question of controlling sampling variance based on the idea of Rao-Blackwellization is also explored. Illustrative examples include identification of multi-component and spatially varying support motions in linear/nonlinear structures.

• Journal of Sensor Science and Technology
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• v.25 no.6
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• pp.418-422
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• 2016

A filtering algorithm based on the decentralized moving average Kalman filter with uncertainties is proposed in this paper. The proposed filtering algorithm presented combines the Kalman filter with the moving average strategy. A decentralized fusion algorithm with the weighted sum structure is applied to the local moving average Kalman filters (LMAKFs) of different window lengths. The proposed algorithm has a parallel structure and allows parallel processing of observations. Hence, it is more reliable than the centralized algorithm when some sensors become faulty. Moreover, the choice of the moving average strategy makes the proposed algorithm robust against linear discrete-time dynamic model uncertainties. The derivation of the error cross-covariances between the LMAKFs is the key idea of studied. The application of the proposed decentralized fusion filter to dynamic systems within a multisensor environment demonstrates its high accuracy and computational efficiency.

• Journal of the Korean Society for Precision Engineering
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• v.7 no.2
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• pp.85-94
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• 1990

In automatic metal cuttings, the chip control is one of the serious problems. So the automatic detection of chip forms is essential to the chip control in automatic metal cuttings. Cutting experiments were carried out under the variety of cutting conditions (cutting speed, feed, depth of cut and tool geometry) and with workpiece made of steel (S45C), and cutting forces were measured in-processing by using a piezoelectric type Tool Dynamometer. In this report, the frequency analysis of dynamic components, the upper frequency distributions, the ratio of RMS values, the numbers of null point and the probability density were calculated from the dynamic componeents of cutting forces filtered through various band pass filters. Experimental results showed that computer chip form monitoring system based on the cutting forces was designed and simulated and that 6 type of chip forms could be detected while in-process machining.