• Journal of Advanced Marine Engineering and Technology
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• v.28 no.2
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• pp.200-209
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• 2004

The fuzzy control based on $\alpha$-level fuzzy set decomposition. It is known to produce quick response and calculating time of fuzzy inference. This paper derived the embodiment computational algorithm for defuzzification by min-max fuzzy inference and the center of gravity method based on $\alpha$-level fuzzy set decomposition. It is easy to realize the fuzzy controller hardware. based on the calculation formula. In addition. this study proposed a circuit that generates PWM actual signals ranging from fuzzy inference to defuzzification. The fuzzy controller was implemented with mixed analog-digital logic circuit using the computational fuzzy inference algorithm by min-min-max and defuzzification by the center of gravity method. This study confirmed that the fuzzy controller worked satisfactorily when it was applied to the position control of a dc servo system.

• Journal of Advanced Marine Engineering and Technology
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• v.19 no.4
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• pp.79-91
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• 1995

Recently, the AC motor which has a simple structure and needs less maintenance has become to be used frequently as a servomotor for position control in the automatic control field. This paper concerns with a method to improve the current and power waveforms of an AC motor, and the current waveforms and 3$\phi$ power waveforms are analyzed by FFT under verious running conditions. The system is composed of a digital bridge inverter, low pass LC filter, and 3$\phi$ AC motor. Through the computer simulations and experiments, it is confirmed that current waveforms and $\phi$ power waveforms con by improved by utilizing the high order low pass LC filter than that of lower order.

• Proceedings of the KACD Conference
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• 2001.11a
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• pp.590.2-590
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• 2001

This study was performed to evaluate the temperature rise on various position of the Buchanan plugger, the peak temperature of plugger's type and the temperature change by its touching time of heat control spring. The System B (Model 1005, Analytic Technologies, USA) and the Buchanan pluggers of F, FM, M and ML are used for this study. The temperature was set to $200^{\circ}C$. The power level on it was set to 10. The heat control spring was touched for 1,2,3,4 and 5 seconds respectively. (omitted)

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.175-175
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• 2000

In this Paper we propose a navigation algorithm which can be used to estimate state vectors such as position and velocity for its motion control using multi-sensor output measurements. The output measurement we will use in estimating the state is a series of known multi-sensor asynchronous outputs with measurement noise. This paper investigates the Extended Kalman Filtering method to merge asynchronous heading, heading rate, velocity of DVL, and SSBL information to produce a single state vector. Different complexity of Kalman Filter, with biases and measurement noise, are investigated with theoretically data from KRISO's AUV. All levels of complexity of the Kalman Filters are shown to be much more close and smooth to real trajectories then the basic underwater acoustic navigation system comment)'used aboard underwater vehicle.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.149-149
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• 2000

We experimented on AGV driving test with color CCD camera which is setup on it. This paper can be divided into two parts. One is image processing part to measure the condition of the guideline and AGV. The other is part that obtains the reference steering angle through using the image processing parts. First, 2 dimension image information derived from vision sensor is interpreted to the 3 dimension information by the angle and position of the CCD camera. Through these processes, AGV knows the driving conditions of AGV. After then using of those information, AGV calculates the reference steering angle changed by the speed of AGV. In the case of low speed, it focuses on the left/right error values of the guide line. As increasing of the speed of AGV, it focuses on the slop of guide line. Lastly, we are to model the above descriptions as the type of PID controller and regulate the coefficient value of it the speed of AGV.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.7 s.172
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• pp.94-101
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• 2005

This work proposes a new method for describing the hysteresis non-linearity of a piezoelectric actuator. The hysteresis behaviour of piezoelectric actuators, including the minor loop trajectory, are modeled by geometrical relationship between a reference major loop and its minor loops. This hysteresis model is transformed into inverse hysteresis model in order to output compensated voltage with regard to the given input displacement. A feedforward neural network, which is trained by a feedback PID control module, is incorporated to the inverse hysteresis model to compensate unknown dynamics of the piezoelectric system. To show the feasibility of the proposed feedforward-feedback controller, some experiments have been carried out and the tracking performance was compared to that of simple PTD controller.

• The Journal of Korea Robotics Society
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• v.11 no.4
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• pp.217-225
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• 2016

Dynamic Positioning (DP) is used to automatically maintain the position and heading of a floating structure subjected to environmental disturbances. A DP control system is composed of a motion controller to compute the desired force and moment and a thrust allocator to distribute the computed force and moment to multiple thrusters considering mechanical and operational constraints. Among various thruster configurations, azimuth thrusters or propeller/rudder pairs tend to make the allocation problem difficult to solve, because these types of propulsion systems include nonlinear constraints. In this paper, a dynamic positioning strategy for a twin-thruster ship that is propelled by two azimuthing thrusters is addressed, and a thrust allocation method which does not require a numerical optimization solver is proposed. The applicability of the proposed method is demonstrated with an experiment using an autonomous boat.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.97-100
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• 2001

This paper introduces that it minimizes interference between links at high speed trajectory tracking of 2-degree parallel link robot. And in spite of system uncertainty, it introduces controller design method which is satisfied with performance specification. To do these, we separate two channels from parallel link robot through ICD(Individual Channel Design) and design controller of each channel using QFT(Quantitative Feedback Theory). Finally, we make sure of robustness and excellence of QFT control1er through simulation and experiment.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2076-2078
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• 2002

This paper presents an Intelligent Balance Beam Controller(IBBC) which can estimate the inertia of load automatically. Balance Beam controller is a kind of construction tool which can control the attitude of the load hanging in the air such as a beam carried by crane. In our previous work, Balance Beam had been built to control the object in air using a mechanical gyro system having a position controllable gimbal structure. In field application the load inertia for operation is not easy to figure out because the weight and shape which determines the inertia, varies depending on the object to be carried. Therefore it is difficult for a worker to operate a Balance Beam and an accident could be caused occasionally. We designed an automatic load estimator to measure the inertia of arbitrary load by using an angular velocity sensor that is installed on the Balance Beam. Simulation results and current status for implementation are presented.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.497-499
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• 1998

the satellite tracking problem of Antenna with two axis of elevation angle and azimuth one is described in this paper. The proposed control procedures for stabilization of nonlinear pedestal unit are consists of a off-line modeling identified by neural network and a on-line neural network controller combined with a reference model using the least square method. the simulation results are introduced and compared to a conventional PID controller.