• Journal of Institute of Control, Robotics and Systems
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• v.22 no.11
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• pp.925-931
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• 2016

The BLDC motor is used widely in industry due to its controllability and freedom from maintenance because there is no mechanical brush in the BLDC motor. Furthermore, it is suitable for high-speed applications, such as compressors and air blowers. For instance, for a compressor with a small impeller due to miniaturizing, the BLDC motor has to rotate at a very high speed to maintain the compression ratio of the compressor. Typically, to reach an ultra-high speed, airfoil bearings must be used in place of ball bearings because of their friction. Unfortunately, the characteristics of airfoil bearings change drastically depending on the revolution speed. In this paper, a BLDC motor with airfoil bearings is controlled with a PID controller. To analyze and determine the PID coefficients, the relay-feedback method is used. Additionally, for adaptive control, a fuzzy logic controller is used. Furthermore, the auto-tuning and self-tuning techniques are combined to control the BLDC motor. The proposed method is able to control the airfoil-bearing BLDC motor efficiently.

• Physical Therapy Korea
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• v.18 no.2
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• pp.67-75
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• 2011

The purpose of this study was to investigate the effects of virtual reality (VR) therapy with compensation inhibition and feedback (CIF) on upper extremity function in chronic stroke patients. Seven chronic stroke patients participated in this study, which was a randomized controlled trial with a crossover design. Self upper extremity exercise, conservative VR therapy, and VR therapy with CIF were performed for one hour per session, 5 times per week, over a 3 week period. The main outcome measures involved range of motion (ROM) including shoulder, elbow, and wrist joints, a Manual Function Test (MFT), and a Motor Activity Log (MAL). Data were calculated as posttest and pretest changes in every session and were analyzed using Friedman and Wilcoxon signed-rank tests at p<.05. The results were as follows: 1) Statistically significant increase in ROM measurements of shoulder and elbow joints were seen with VR therapy with CIF compared to VR therapy and self upper extremity exercise (p<.05), whereas no significant increasing was noted for the wrist joint (p>.05). 2) Statistically significant increase in the MFT was seen with VR therapy with CIF compared with VR therapy and self upper extremity exercise (p<.05). 3) VR therapy with CIF also resulted in statistically significant increase in both activity of use (AOU) (p<.05) and quality of movement (QOM) (p<.05) on the MAL test when compared with VR therapy and self upper extremity exercise, respectively. In conclusion, VR therapy with CIF was more effective than conservative VR therapy and self upper extremity exercise in improving the upper extremity function in hemiplegic patients with chronic stroke.

• ETRI Journal
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• v.31 no.6
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• pp.755-764
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• 2009

This paper presents a switched self-biasing and a tail current-shaping technique to suppress the 1/f noise from a tail current source in differential cross-coupled inductance-capacitance (LC) voltage-controlled oscillators (VCOs). The proposed LC VCO has an amplitude control characteristic due to the creation of negative feedback for the oscillation waveform amplitude. It is fabricated using a 0.13 ${\mu}m$ CMOS process. The measured phase noise is -117 dBc/Hz at a 1 MHz offset from a 4.85 GHz carrier frequency, while it draws 6.5 mA from a 0.6 V supply voltage. For frequency tuning, process variation, and temperature change, the amplitude change rate of the oscillation waveform in the proposed VCO is 2.1 to 3.2 times smaller than that of an existing VCO with a fixed bias. The measured amplitude change rate of the oscillation waveform for frequency tuning from 4.55 GHz to 5.04 GHz is 131 pV/Hz.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.50-59
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• 1998

This paper presents stabilization and adaptive control of flexible single link robot manipulator system by self-recurrent neural networks that is one of the neural networks and is effective in nonlinear control. The architecture of neural networks is a modified model of self-recurrent structure which has a hidden layer. The self-recurrent neural networks can be used to approximate any continuous function to any desired degree of accuracy and the weights are updated by feedback-error learning algorithm. When a flexible manipulator is rotated by a motor through the fixed end, transverse vibration may occur. The motor toroque should be controlled in such a way that the motor rotates by a specified angle, while simultaneously stabilizing vibration of the flexible manipuators so that it is arresed as soon as possible at the end of rotation. Accurate vibration control of lightweight manipulator during the large changes in configuration common to robotic tasks requires dynamic models that describe both the rigid body motions, as well as the flexural vibrations. Therefore, a dynamic models for a flexible single link robot manipulator is derived, and then a comparative analysis was made with linear controller through an simulation and experiment. The results are proesented to illustrate thd advantages and imporved performance of the proposed adaptive control ove the conventional linear controller.

• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.3
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• pp.240-245
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• 2004

This paper presents a Switched Reluctance Motor(SRM) drive using the self-tuning control method to achieve the maximum torque. SRM has the difficulty to research it by an analytic method and to control the speed End torque because of the high nonlinearity. So, in this paper, the self-tuning control method is applied to relevantly controlling turn-on/off angle to operate at the maximum torque. Also, the feedback signals to control the turn-on/off angle are the encoder pulse and the increment of phase current. At first, n adequate turn-off angle is searched by itself and then a turn-on angle is done. As the relationship between turn-on and him-off angle is mutual dependent, the turn-on/off angle is controlled by a real time self-tuning control method in order to maintain the maximum torque. The proposed self-tuning Algorithm is verified by experiments.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.6
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• pp.29-36
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• 2015

This paper presents an LED backlight driver IC consisting of three linear current regulators and an output-voltage regulation loop with a self-adjustable reference voltage. In the proposed LED driver, the output voltage is controlled by dual feedback loops. The first loop senses and controls the output voltage, and the second loop senses the voltage drop of the linear current regulator and adjusts the reference voltage. With these feedback loops, the voltage drop of the linear current regulator is maintained at a minimum value, at which the driver efficiency is maximized. The output of the driver is a three-channel LED setup with four LEDs in each channel. The luminance is adjusted by the PWM dimming signal. The proposed driver is designed by a $0.35-{\mu}m$ 60-V high-voltage process, resulting in an experimental maximum efficiency of approximately 85%.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.353-356
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• 1999

A 3.3V PLL(Phase Locked loop) is designed for a high frequency, low voltage, and low power applications. This paper proposes a new PLL architecture to improve voltage to frequency linearity of VCO(Voltage controlled oscillator) with new delay cell. The proposed VCO operates at a wide frequency range of 30MHz~1㎓ with a good linearity. The DC-DC voltage up/down converter is utilized to regulate the control voltage of the two-stage VCO. The designed PLL architecture is implemented on a 0.6${\mu}{\textrm}{m}$ n-well CMOS process. The simulation results show a locking time of 2.6$\mu$sec at 1Hz, Lock in range of 100MHz~1㎓, and a power dissipation of 112㎽.

• Journal of the Semiconductor & Display Technology
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• v.9 no.1
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• pp.1-4
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• 2010

We investigated a linear carbon nanotube motor serving as the key building block for nano-scale motion control by using molecular dynamics simulations. This linear nano-motor, is based on the electrostatically telescoping multi-walled carbon-nanotube with ultralow intershell sliding friction, is controlled by the gate potential with the capacitance feedback sensing. The resonant harmonic peaks are induced by the interference between the driving frequencies and its self-frequency. The temperature is very important factor to operate this nanomotor.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.436-438
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• 1993

A rule based fuzzy expert system in self-tune PID controllers is presented in this paper. The rule base. the core of the expert system, is extracted from the Wills' tuning map and the author's knowledge about the implicit relations between PID gains and controlled output response. The overall control system consists of the relay feedback scheme and the expert system, where the one is responsible for initial tuning and the other for subsequent tuning. The PID control system with the proposed fuzzy expert system, shows better convergence rate and control performances than those of a Litt in spite of the fact that the two rule bases are extracted from the same maps provided by Wills.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.312-321
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• 2003

In this paper, we implement robot which are ability to recognize obstacles and moving automatically to destination. we present two results in this paper; hardware implementation of image processing board and software implementation of visual feedback algorithm for a self-controlled robot. In the first part, the mobile robot depends on commands from a control board which is doing image processing part. We have studied the self controlled mobile robot system equipped with a CCD camera for a long time. This robot system consists of a image processing board implemented with DSPs, a stepping motor, a CCD camera. We will propose an algorithm in which commands are delivered for the robot to move in the planned path. The distance that the robot is supposed to move is calculated on the basis of the absolute coordinate and the coordinate of the target spot. And the image signal acquired by the CCD camera mounted on the robot is captured at every sampling time in order for the robot to automatically avoid the obstacle and finally to reach the destination. The image processing board consists of DSP (TMS320VC33), ADV611, SAA7111, ADV7l76A, CPLD(EPM7256ATC144), and SRAM memories. In the second part, the visual feedback control has two types of vision algorithms: obstacle avoidance and path planning. The first algorithm is cell, part of the image divided by blob analysis. We will do image preprocessing to improve the input image. This image preprocessing consists of filtering, edge detection, NOR converting, and threshold-ing. This major image processing includes labeling, segmentation, and pixel density calculation. In the second algorithm, after an image frame went through preprocessing (edge detection, converting, thresholding), the histogram is measured vertically (the y-axis direction). Then, the binary histogram of the image shows waveforms with only black and white variations. Here we use the fact that since obstacles appear as sectional diagrams as if they were walls, there is no variation in the histogram. The intensities of the line histogram are measured as vertically at intervals of 20 pixels. So, we can find uniform and nonuniform regions of the waveforms and define the period of uniform waveforms as an obstacle region. We can see that the algorithm is very useful for the robot to move avoiding obstacles.