• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.331-334
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• 2003

In this paper, on the subject of a speed control wind turbine, the type of wind speed reference decision between conventional MPPT tracking speed control and MPPT with LPF(Low Pass Filter) speed control algorithm are introduced and its performances are compared using a model based on MATLAB Simulink, and to get more realistic output data, the stored wind data as its wind speed input from 30kW wind power system in Buan, Haechang is used.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.9-16
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• 2018

Predictive control is a very practical method that obtain the current input that minimizes the future errors of the reference command and state by use of the predictive model of the controlled object, and can also consider the constraints of the state and input. Although there have been studies in which predictive control is applied to mobile robots, performance has not been optimized as various control parameters for determining control performance have been arbitrarily specified. In this paper, we apply the genetic algorithm to the trajectory tracking control of a mobile robot with input constraints in order to minimize the trajectory tracking errors through control parameter tuning, and apply the quadratic programming Hildreth method to reflect the input constraints. Through the computer simulation, the superiority of the proposed method is confirmed by comparing with the existing method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.698-707
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• 2017

This paper deals with predictive control methods of mobile robots for reference trajectory tracking control. Predictive control methods using predictive model are known as effective schemes that minimize the future errors between the reference trajectories and system states; however, the amount of real-time computation for the predictive control are huge so that their applications were limited to slow dynamic systems such as chemical processing plants. Lately with high computing power due to advanced computer technologies, the predictive control methods have been applied to fast systems such as mobile robots. These predictive controllers have some control parameters related to control performance. But these parameters have not been optimized. In this paper we employed the genetic algorithm to optimize the control parameters of the predictive controller for mobile robots. The improved performances of the proposed control method are demonstrated by the computer simulation studies.

• 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.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.426-429
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• 1989

The robust self-tuning controller is designed which is guaranteed the asymptotic regulation and tracking in the presence of a bounded parameter perturbation. The global stability in the presence of a finite noise and disturbance is ensured. The controller has a error driven structure, and involves the common model of a disturbance and reference input in the internal model. The adaptive system tunes the controller parameters such that the quadratic performance index which involves a weighting factor is optimized.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.940-945
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• 2009

This paper presents the design method of low order controller for the active pantograph of electric train system. The pantograph is the most playa role to supply constant current to the train. The design objectives are to have good tracking performance about reference contact force despite the stiffness variation that is like sinusoidal function concerned in train speed or span length of contact wire. In this paper, we consider stiffness variation from external disturbance of active pantograph to simplify model equation, and propose simple second-order controller which is designed by Characteristic ratio assignment(CRA) control method. Finally, we verify time response appling to model equation of real system and frequency response about parameter uncertainty like stiffness variation. it is performed by Matlab version 6.5 and Matlab simulink simulation.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.6
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• pp.1141-1149
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• 1992

This paper presents a robust centralized control scheme for a magnetic bearing system which supports a rigid rotor at both shaft ends in the radial direction. The negative stiffness element and the inductive force associated with bearing magnetic field are considered in the dynamic model of the system. For this model, the controllability and observability are examined, and then a robust control theory is applied to design two types of multi-input multi-output servocontrollers. A general servocompensator is embedded in the first one and a centralized PID controller is suggested as a second one. By simulation study, the performance of two types of servocontrollers are compared in the aspects of disturbance rejection, reference tracking and the robustness limit.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.531-536
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• 2003

In this paper, a redesigned guidance and control system for a lunar lander is presented. In past studies, the authors developed a trajectory and attitude control system which achieves the vertical soft landing on the lunar surface. It is confirmed that the system has a good tracking ability to a predefined profile and good robustness against a thruster failure mode where a partial failure of clustered engines was assumed. However, under the previous control laws, the landing point tends to be shifted, in response to the system parameter values, from a target point. Also, an unbalanced moment due to a thruster failure mode was not considered in the simulation. Therefore, in this study, the downrange control is added to the system to enable the vehicle to land at a pre-assigned target point accurately. Furthermore, inhibiting the effect of the unbalanced moment is attempted thorough redesigning the attitude control system. A numerical simulation was performed to confirm the ability of the proposed system with regard to the above problems. Moreover, in the past simulations, a low initial altitude was assumed as an initial condition: in this study, however, the performance of the proposed system is examined over the whole trajectory from an initial altitude of 10 [km] to the lunar surface.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.185-191
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• 2008

In this paper, experimental studies of a neural network (NN) control technique for non-model based position control of the x-y table robot are presented. Decentralized neural networks are used to control each axis of the x-y table robot separately. For an each neural network compensator, an inverse control technique is used. The neural network control technique called the reference compensation technique (RCT) is conceptually different from the existing neural controllers in that the NN controller compensates for uncertainties in the dynamical system by modifying desired trajectories. The back-propagation learning algorithm is developed in a real time DSP board for on-line learning. Practical real time position control experiments are conducted on the x-y table robot. Experimental results of using neural networks show more excellent position tracking than that of when PD controllers are used only.