• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.4
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• pp.361-370
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• 1992

A controller of the hovering VTOL aircraft with four fan is constructed by SRFIMF(State Rate Feedback Implicit Model-Following)theory, in which feedback state are angle acceleration, angle velocity and angle position of the aircraft during hover With yaw control of the system, characteristics of the hovering aircraft can be analyzed by changing states feedback gain and sponse provides robust stable hovering system.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2714-2716
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• 2005

In this paper, we designed the adaptive fuzzy logic controller for crane system using neural network and real-coding genetic algorithm. The proposed algorithm show a good performance on convergence velocity and diversity of population among evolutionary computations. The weights of neural network is adaptively changed to tune the input/output gain of fuzzy logic controller. And the genetic algorithm was used to leam the feedforward neural network. As a result of computer simulation, the proposed adaptive fuzzy logic controller is superior to conventional controllers in moving and modifying the destination point.

• Earthquakes and Structures
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• v.6 no.2
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• pp.217-235
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• 2014

In this paper, different feedback control strategies are presented for active seismic control using proportional-integral-derivative (PID) type controllers. The parameters of PID controller are found by using an numerical algorithm considering time delay, maximum allowed control force and time domain analyses of shear buildings under different earthquake excitations. The numerical algorithm scans combinations of different controller parameters such as proportional gain ($K_p$), integral time ($T_i$) and derivative time ($T_d$) in order to minimize a defined response of the structure. The controllers for displacement, velocity and acceleration feedback control strategies are tuned for structures with active control at the first story and all stories. The performance and robustness of different feedback controls on time and frequency responses of structures are evaluated. All feedback controls are generally robust for the changing properties of the structure, but acceleration feedback control is the best one for efficiency and stability of control system.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.157-160
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• 2001

This paper presents A Design of WMR Controller that being composed of cooperative relation between PID controller and optimized neural network algorithm, it operate a variable control by velocity. Some proposed algorithm in the past just depended on PID controller for the control of position of WMR but for more efficient control we design a variable controller that operate control by PD controller using neural network if it is satisfied with any given condition. it adjust gain of PD controller for real time control using a fast feedforward algorithm which is different with Form of the standard backpropagation algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.132.5-132
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• 2001

In teleoperation field robotic system such as hydraulically actuated robotic excavator, the maneuverability and convenience is the most important part in the operation of robotic excavator. Particularly the force information is important in dealing with digging and leveling operation in the teleoperated excavator. Excavators are also subject to a wide variation of soil-tool interaction forces. This paper presents a new force reflecting joystick in a velocity-force type bilateral teleoperation system. The master system is electrical joystick and the slave system is hydraulically actuated cylinder with linear position sensor. Particularly Pneumatic motor is used newly in the master joystick for force reflection and the information of the pressure of salve cylinder is measured and utilized as the force feedback signal. Also force-reflection gain greatly affects the ...

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.180.5-180
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• 2001

This paper presents A Design of WMR Controller that being composed of cooperative relation between PID controller and optimized neural network algorithm, it operate a variable control by velocity. Some proposed algorithm in the past just depended on PID controller for the control of position of WMR but for more efficient control we design a variable controller that operate control by PD controller using neural network if it is satisfied with any given condition. it adjust gain of PD controller for real time control using a fast feedforward algorithm which is different with Form of the standard backpropagation algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.65.6-65
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• 2002

In teleoperation robotic system such as hydraulically actuated robotic excavator for dangerous area, the maneuverability and convenience is the most important part. Particularly the force information is important in dealing with digging and leveling operation in the tole-operated excavator. Excavators are also subject to a wide variation of soil-tool interaction forces. This paper proposes a new force reflecting joystick using pneumatic motor and a new algorithm for selecting force-reflecting gain in a velocity-force type bi lateral teleoperation system. The master system is electrical joystick with the same structure of that of real excavator. Particularly Pneumatic motor is used newly in...

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.350-354
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• 2002

In this paper, we designed an autopilot of the unmanned piloted vehicle to guide to the specific position, and analyzed robustness of the designed autopilot. We divide an aircraft velocity into the three case which are low, cruising and high speed, and designed autopilot gains are gain scheduled. We generated the turbulence for the operational altitudes and proved robustness of the designed autopilot for the turbulence using simulation.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1121-1124
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• 1999

Linear motor is able to produce line movement without rotary-to-line converter at the system required line moving. Thus Linear motor has no gear, screw, belt for line movement. Therefore it has some advantage which decrease friction loss, noise, vibration, maintenance effort and prevent decay of control performance due to backlash. This paper proposes the estimation method of unknown parameters from the BLDC Linear motor and determine the PI controller gain through this estimation. Each control movement that is current, speed, position control, and PWM wave generation is performed on Processor, which is DSP(Digital Signal Processor), having high speed performance. PI theory is adopted to each for controller for control behavior More fast convergence to command position is accomplished by applying the new velocity locus which derived from position error.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.10
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• pp.44-52
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• 1994

In this paper, we assume that the dynamics of DC motor and nonlinear load are unknown. We propose an inverse dynamic model of DC motor and nonlinear load using the artificial neural network and construck speed control system based on the proposed dynamic model. We also propose another dynamic model with speed prediction scheme using the artificial neural network that removes the undesirable time delay effect caused by the computation time during the real-time control. We suggest a dynamic model which has arbitrary number of speed arguments and is especially effective when the motor and load has large moment of inertia. Next, we suggest a controller that combine the neurocontrol and PID control with constant gain. We show that the proposed neurocontrol systems have capabilities of noise rejection and generalization to have good velocity tracking through computer simulations and experiments.