• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.659-666
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• 2006

Servo-motors are used as key components of automated system by performing precise motion control as accurate positioning and accurate speed regulation in response to the commands from computers and sensors. Especially, the linear brushless servo-motors have numerous advantages over the rotary servo motors which have connection with the friction induced transfer mechanism such as ball screws, timing belts, rack/pinion. This paper proposes an estimation method of unknown motor system parameters using the informations from the sinusoidal driving type linear brushless DC motor dynamics and outputs. The estimated parameters can be used to tune the controller gain and a disturbance observer. In order to meet this purpose high performance Digital Signal Processor, TMS320F240, designed originally for implementation of a Field Oriented Control(FOC) technology is adopted as a controller of the liner BLDC servo motor. Having A/D converters, PWM generators, rich I/O port internally, this servo motor application specific DSP play an important role in servo motor controller. This linear BLDC servo motor system also contains IPM(Intelligent Power Module) driver and hail sensor type current sensor module, photocoupler module for isolation of gate signals and fault signals.

• Journal of Advanced Navigation Technology
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• v.9 no.1
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• pp.41-47
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• 2005

In this paper, the performances of various channel estimation schemes for DVB-H system are investigated. The linear interpolation, the second order interpolation, the cubic spline interpolation, and the time-domain interpolation, which are used in frequency domain, are chosen as the channel estimation schemes. We derived the performances of Mean Square Error (MSE), Bit Error Rate (BER), and the complexity of calculation in Rayleigh and Rician fading channel through computer simulations. From the simulation results, the cubic spline interpolation shows the best performance under high $E_b/N_0$ environments.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.10
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• pp.990-999
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• 2007

We deal with second order NMP(Non-Minimum Phase) systems which are difficult to control with conventional methods because of their inherent characteristics of undershoot. In such systems, reducing the undesirable undershoot phenomenon makes the response time of the systems much longer. Moreover, it is impossible to control the magnitude of undershoot in a direct way and to predict the response time. In this paper, we propose a novel two sliding mode control scheme which is capable of determining the magnitude of undershoot and thus the response time of NMP systems a priori. To do this, we introduce two sliding lines which are in charge of control in turn. One is used to stabilize the system and achieve asymptotic regulation eventually like the conventional sliding mode methods and the other to stably control the magnitude of undershoot from the beginning of control until the state meets the first sliding line. This control scheme will be proved to have an asymptotic regulation property. The computer simulation shows that the proposed control scheme is very effective and suitable for controlling the second order NMP system because it can decide the magnitude of undershoot in a direct and stable way and reduce the response time compared with the conventional ones.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.5
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• pp.1319-1324
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• 1998

The phase and frequency commands of a ratating radar system that utilizes frequency scanning to steer the beam in the azimuth direction and phase shifters in the elevation direction are derived in terms of the angles of the groung based coordinate system. The antenna type considered is slotted arrays that are easy to construct at such high microwave frequency as the X band. The frequency that has non-linear characteristics as a functio ofthe elevation angle is plotted and the derived frequency equation is aproximated to be a simple form to reduce the calculation time for real time multi-function radar systems. It is shown that the approximated frequency command is in good agreement with the exact one if the range of azimuth scanning is limited by ${\pm}10^{\circ}$.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.432-441
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• 2000

This paper deals with the fuzzy modeling for the complex and uncertain nonlinear systems, in which conventional and mathematical models may fail to give satisfactory results. Genetic algorithm has been used to identifY parameters and structure of fuzzy model because it has the ability to search optimal solution somewhat globally. The genetic algorithm, however, has a problem, which optimization process can be premature convergence in the case of lack of genetic divergence of population. Virus- evolutionary genetic algorithm(VEGA) could be a strategy against this local convergence. Therefore, we use VEGA for fuzzy modeling. In this method, local information is exchanged in population so that population can sustain genetic divergence. finally, to prove the theoretical hypothesis, we provide numerical examples to evaluate the feasibility and generality of fuzzy modeling using VEGA.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.208-216
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• 1996

This paper presents a decentralized control scheme for interconnected systems with unmodeled nonlinearities and interactions using a neural coordinator. The interactions due to the interconnection and the unmodeled nonlinearity associated with each subsystem are represented by the deviations from linearized states of decomposed subsystems. the decentralized controller is composed of local controllers and a neural coordinator. The local controller for each subsystem is derived from linearized local system parameters y linear optimal control theory. the neural cooridnator generates a corrective control signal to cancel the effect of deviation sthrough the backpropagation learning with the rrors obtained form the difference of the local system outputs and reference model outputs. the reference model consists of the part of local system without deviations. The effectiveness of the proposed control scheme is demonstrated by simulation studies.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1872-1873
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• 2011

This paper presents a discrete-time approximate linear model of the continuous-time pulse-width-modulated linear system, and then, by employing the resultant model, a simple LQ suboptimal control scheme is proposed for a DC-DC step-down buck converter. The proposed scheme effectively regulates the output voltage to the desired level, which is also verified by the numerical simulation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.991-1000
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• 1994

To control nonlinear systems adaptively, we improve learning speed of neural networks and present a novel control algorithm characterized by compensation of control inputs. In an error-backpropagation algorithm for tranining multilayer neural networks(MLNN's) the effect of the slope of activation functions on learning performance is investigated and the learning speed of neural networks is improved by auto-adjusting the slope of activation functions. The control system is composed of two MLNN's, one for control and the other for identification, with the weights initialized by off-line training. The control algoritm is modified by a control strategy which compensates the control error induced by the indentification error. Computer simulations show that the proposed control algorithm is efficient in controlling a nonlinear system with abruptly changing parameters.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.522-524
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• 1999

This paper addresses the systematic approach to the fuzzy modeling of the class of complex and uncertain nonlinear systems. While the conventional genetic algorithm (GA) only searches the global solution, Virus-Evolutionary Genetic Algorithm(VEGA) can search the global and local optimal solution simultaneously. In the proposed method the parameter and the structure of the fuzzy model are automatically identified at the same time by using VEGA. To show the effectiveness and the feasibility of the proposed method, a numerical example is provided. The performance of the proposed method is compared with that of conventional GA.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.12
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• pp.2519-2526
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• 2002

A disturbance observer-based vehicle stability controller is proposed in this paper. The lumped disturbance to the vehicle yaw rate dynamics caused by the uncertain factors such as uncertain tire forces and parameters is estimated by the disturbance observer, which is utilized by the robust controller to stabilize the lateral dynamics of the vehicle. The dynamics of the hydraulic actuator is incorporated in the vehicle stability controller design using the model reduction technique. Modular control design methodology is adopted to effectively deal with the mismatched uncertainty. Simulation results indicate that the proposed disturbance observer-based vehicle stability controller can achieve the desired reference tracking performance as well as sufficient level of robustness.