• Transactions on Control, Automation and Systems Engineering
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• v.3 no.3
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• pp.176-180
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• 2001

In this papers, we derive a simple kinematic and dynamic formulation of an unmanned electric bicycle. We also check the controllability of the stabilization problem of bicycle. We propose a new control algorithm for the self stabilization of unmanned bicycle with bounded wheel speed and steering angle by using nonlinear control based on the sliding patch and stuck phenomena which was introduced by W. Ham. We also propose a sort of optimal control strategy for steering angle and driving wheel speed that make the length of bicycle\`s path be the shortest. From the computer simulation results, we prove the validity of the proposed control algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1327-1334
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• 1999

The stabilization control of ball-beam system is difficult because of its nonlinearity and structural unstability. Futhermore, a series of classical methods such as the PID and the full state feedback controller(FSFC) based on the local linearizations have narrow stabilizable regions. At the same time, the fine tunings of their gain parameters are also troublesome. Therefore, in this paper, three improved design techniques of stabilization controller for a ball-beam system were proposed. These parameter tuning methods in the double PID controller(DPIDC), the FSFC and the a sliding mode controller(SMC) were dependent upon the Real Value Elitist Genetic Algorithm (RVEGA). Finally, by applying the DPIDC, the FSFC and the Real Variable Elitist Genetic Algorithm based Sliding Mode Control(RVEGA SMC) to the stabilizations of a ball-beam system, the performances of the RVEGA SMC technique were showed to be superior to those of two other type controllers.

• Journal of the Korean Institute of Telematics and Electronics
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• v.21 no.6
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• pp.105-114
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• 1984

A fast pseudo-noise (PN) sequence acquisition algorithm for the direct-sequence (DS) spread spectrum system is proposed. The basic concept of the algorithm has been adopted from that of the classical sliding correlator. Mathematical modeling, analysis and computer simulation of the proposed system have been done. The results of analysis and computer simulation show that the acquisition system yields a significant performance improvement over the sliding correlator. Its acquisition time takes only 45 ms when signal-to-noise ratio(SNR) is -18dB. The algorithm developed has been implemented in hardware and its experimental result is also given.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.182-182
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• 2000

This paper proposes a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a robot. Using this method, the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. And, it is guaranteed that the selected solution become the global optimal solution by optimizing the Akaike's information criterion. The trajectory trucking experiment of the polishing robot system shows that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding model controller provides reliable tracking performance during the polishing process.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2255-2257
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• 2001

This paper introduces a indirect rotor position and speed estimation algorithm for the SRM(switched reluctance motor) sensorless control, based on the sliding mode observer. The information of position and speed is generally provided by encoder or resolver. However, the position sensor not only adds complexity, cost, and size to the whole drive system, but also causes limitation for industrial applications. In this paper, in order to eliminate the position sensor, indirect position sensing method using sliding mode observer is used for SRM drives. And this observer parameters are optimized by evolutionary algorithm. PI controller is also optimized for the SRM to track precisely using evolutionary algorithm.

• Journal of Electrical Engineering and Technology
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• v.7 no.6
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• pp.1014-1022
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• 2012

This paper proposes a modified recursive sliding DFT to measure the phase of a single-tone. The modification is to provide a self error-cancelling mechanism so that it can significantly reduce the numerical error, which is generally introduced and accumulated when a recursive algorithm is implemented in finite wordlength arithmetic. The phase measurement error is analytically derived to suggest optimized distributions of quantization bits. The analytic derivation and the robustness of the algorithm are also verified by computer simulations. It shows that the maximum phase error of less than $5{\times}10^{-2}$ radian is obtained even when the algorithm is coarsely implemented with 4-bit wordlength twiddle factors.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.26-28
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• 2004

The sliding mode control law provides a robust solution for general control problems. Most real systems which use a portable hard disk drive have to overcome disturbances and model uncertainties for proper operation. The chattering effect caused from unexpected oscillation can make the system be unstable. Therefore, we propose a robust control algorithm for the nonlinear second order systems with model uncertainties and disturbances. The proposed algorithm is designed following a sliding mode and observer based control. Thus the proposed algorithm has more expanded bounded region of control. Simulation results show the robustness of the proposed controller.

• Journal of Multimedia Information System
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• v.6 no.1
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• pp.1-6
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• 2019

In the field of industrial applications, the real-time performance of the target detection problem is very important. The most serious time consumption in the pedestrian detection process is the extraction phase of the candidate window. To accelerate the speed, in this paper, a fast extraction of pedestrian candidate window based on the BING (Binarized Normed Gradients) algorithm replaces the traditional sliding window scanning. The BING features are extracted with the positive and negative samples and input into the two-stage SVM (Support Vector Machine) classifier for training. The obtained BING template may include a pedestrian candidate window. The trained template is loaded during detection, and the extracted candidate windows are input into the classifier. The experimental results show that the proposed method can extract fewer candidate window and has a higher recall rate with more rapid speed than the traditional sliding window detection method, so the method improves the detection speed while maintaining the detection accuracy. In addition, the real-time requirement is satisfied.

• Journal of IKEEE
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• v.8 no.1 s.14
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• pp.128-135
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• 2004

This paper proposes a phase measurement algorithm which is based on the recursive implementation of sliding-DFT. The algorithm is designed to have a robust behavior against the erroneous factors of frequency drift, additive noise, and twiddle factor approximation. The size of phase error caused by the finite wordlength implementation of DFT twiddle factors is shown significantly lower than that of magnitude error. The drastic reduction of the phase error is achieved by the exploitation of the quadruplet symmetry characteristics of the approximated twiddle factors in the complex plane. Four channel power-line phase measurement system is also designed and implemented based on the time-multiplexed sharing architecture of the proposed algorithm. The operation of the developed system is also verified by the experiment performed under the test environment implemented with the multi-channel function generator and the on-line interfaced host processor system. The proposed algorithm's features of phase measurement accuracy and its robustness against the finite wordlength effects can provide a significant impact especially for the ASIC or microprocessor based embedded system applications where the enhanced processing speed and implementation simplicity are crucial design considerations.

• Journal of Drive and Control
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• v.14 no.2
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• pp.30-36
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• 2017

This paper presents a sliding mode observer-based fault detection algorithm for steering inputs of an all-terrain crane. All-terrain cranes with multi-axles have several steering modes for various working purposes. Since steering angles at the other axles except the first wheel are controlled by using the information of steering angle at the first wheel, a reliable signal of the first axle's steering angle should be secured for the driving safety of cranes. For the fault detection of steering input signal, a simplified crane model-based sliding mode observer has been used. Using a sliding mode observer with an equivalent output injection signal that represents an actual fault signal, a fault signal in steering input was reconstructed. The road steering mode of the crane's steering system was used to conduct performance evaluations of a proposed algorithm, and an arbitrary fault signal was applied to the steering angle at the first wheel. Since the road steering mode has different steering strategies according to different speed intervals, performance evaluations were conducted based on the curved path scenario with various speed conditions. The design of algorithms and performance evaluations were conducted on Matlab/Simulink environment, and evaluation results reveal that the proposed algorithm is capable of detecting and reconstructing a fault signal reasonably well.