• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.6
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• pp.531-536
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• 2002

This paper investigates the problem of the speed controller of conventional speed sensorless stator flux-oriented(SFO) induction motor drive, and proposes a robust speed controller to solve the conventional problem. In the proposed method, a robust speed controller for speed sensorless SFO system Is designed by taking advantage of disturbance torque observer and using feedforward.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.779-782
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• 1996

Repetitive control is a proposed control strategy in view of the internal model principle and achieves a high accuracy asymptotic tracking property by implementing a model that generates the periodic signals of period into the closed-loop system. Since the repetitive control system contains a periodic signal generator with positive feedback loop, which reduces the stability margin, in the overall closed-loop system, the stability of the closed-loop system should be considered as an important problem. In case that a real system has plant uncertainties which are not represented through modeling, the robust stability problem of the repetitive control system has not been considered sufficiently. In this paper, we propose the robust stability condition for the system with modeling uncertainty. The proposed robust stability condition will be obtained using the robust performance condition in the H$_{\infty}$ control. Moreover, by use of the proposed robust stability condition, we propose a procedure that designs a repetitive controller and a feedback controller simultaneously which can stabilize the overall closed-loop system robustly and which can also do the closedloop system without repetitive controller..

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2643-2645
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• 2001

In this paper, a problem of limited sampling time that occurs essentially in digital data acquisition system is discussed. Low sampling frequency and a lot of processed data in micro-controller interferes normal operation of the controller from acquiring information in some case. To settle this problem, we design the sub-system which consists of a new simple micro-controller. which is managed the particular sensor making the trouble to main-controller system.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2989-2991
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• 1999

We design the stable controller with variable gains and reference velocity in order to apply to this controller the proper gains and reference velocity generated with fuzzy logic in on-line. The proposed controller is applied to solve two problems including the reference velocity tracking problem and speedy tracking problem. The result of the simulation show a robust performance under the different conditions

• Journal of the Semiconductor & Display Technology
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• v.16 no.4
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• pp.46-51
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• 2017

Recent controller design techniques often discretize the target system and implement a discrete controller that is digitized to match the target system. When constructing such a discrete system, it is necessary to first determine the sampling time. The smaller the sampling time is, the more advantageous it can be made similar to the original system, but the cost is a problem when realizing such a configuration as hardware. On the other hand, the longer the time, the more different the system is from the original system, and eventually the control becomes impossible. In this paper, we consider the above problem and propose a more logical approach to determine the sampling time in the discrete system and investigate the relation with the differential controller. We also apply this process to a nonlinear system called ARAGO disc and verify its validity through computer simulation.

• Structural Engineering and Mechanics
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• v.81 no.1
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• pp.51-57
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• 2022

The problem of optimal stochastic GA control of the system with uncertain parameters and unsure noise covariates is studied. First, without knowing the explicit form of the dynamic system, the open-loop determinism problem with path optimization is solved. Next, Gaussian linear quadratic controllers (LQG) are designed for linear systems that depend on the nominal path. A robust genetic neural network (NN) fuzzy controller is synthesized, which consists of a Kalman filter and an optimal controller to assure the asymptotic stability of the discrete control system. A simulation is performed to prove the suitability and performance of the recommended algorithm. The results indicated that the recommended method is a feasible method to improve the performance of active tuned mass damper (ATMD) shear buildings under random earthquake disturbances.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.4
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• pp.502-508
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• 2000

The amount of container freight is continuously increasing recently, but freight congestion problem frequently occurs at ports due to low efficiency of container crane in transportation and cargo handling. In this paper, a method for designing a fuzzy controller of the container crane system is presented. In this scheme a mathematical model for the system is obtained in state space representation. The response of the proposed fuzzy controller is compared with that of the optimal controller at the same condition. Through the simulation results, the performance of the fuzzy controller was observed better than that of optimal controller in respect of reference change, disturbances and parameter change. The fuzzy controller was also more stable and robust than the optimal controller.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2365-2377
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• 2017

This paper addresses two interrelated problems concerning the tracking control of pod propulsion unmanned surface vessel (USV), namely, the modeling of pod propulsion USV, and tracking controller design. First, based on MMG modeling theory, the model of pod propulsion USV is derived. Furthermore, a practical adaptive neural tracking controller is proposed by backstepping technique, neural network approximation and adaptive method. Meanwhile, unlike some existing tracking methods for surface vessel whose control algorithms suffer from "explosion of complexity", a novel neural shunting model is introduced to solve the problem. Using a Lyapunov functional, it is proven that all error signals in the system are uniformly ultimately bounded. The advantages of the paper are that first, the underactuated characteristic of pod propulsion USV is proved; second, the neural shunting model is used to solve the problem of "explosion of complexity", and this is a combination of knowledge in the field of biology and engineering; third, the developed controller is able to capture the uncertainties without the exact information of hydrodynamic damping structure and the sea disturbances. Numerical examples have been given to illustrate the performance and effectiveness of the proposed scheme.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.101-105
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• 1989

The iterative learning controller makes the system output follow the desired output over a finite time interval through iterating trials. In this paper, first we discuss that the design problem of learning controller is originally the design problem of the inverse model. Then we show that the tracking error which is the difference between the desired output and the system output is reduced monotonically by properly modeled inverse system if the magnitude of the learning operator being introduced is bounded within the unit circle in complex domain. Also it would be shown that the conventional learning control method is a kind of extremely simplified inverse model learning control method of the objective controlled system. Hence this control method can be considered as a generalization of the conventional learning control method. The more a designer model the objective controlled system precisely, the better the performance of the approximated inverse model learning controller would be. Finally we compare the performance of the conventional learning control method with that of the approximated inverse model learning control method by computer simulation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.10 s.253
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• pp.1209-1218
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• 2006

Precision servomechanisms are widely used in machine tool, semiconductor and flat panel display industries. It is important to improve contouring accuracy in high-precision servomechanisms. In order to improve the contouring accuracy, cross-coupled control systems have been proposed. However, it is very difficult to select the controller parameters because cross-coupled control systems are multivariable, nonlinear and time-varying systems. In this paper, in order to improve contouring accuracy of a biaxial servomechanism, a cross-coupled controller is adopted and an optimal tuning procedure based on an integrated design concept is proposed. Strict mathematical modeling and identification process of a servomechanism are performed. An optimal tuning problem is formulated as a nonlinear constrained optimization problem including the relevant controller parameters of the servomechanism. The objective of the optimal tuning procedure is to minimize both the contour error and the settling time while satisfying constraints such as the relative stability and maximum overshoot conditions, etc. The effectiveness of the proposed optimal tuning procedure is verified through experiments.