• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1998.10a
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• pp.159-165
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• 1998

In this paper the sway-control problem of a container crane is investigated. The control loop is divided into two stages. The first stage is a modified time optimal control for trolley traversing. The velocity command for trolley traversing consists of three components ; a reference velocity and two feedback signals for compensating the deviations of trolley and sway angle from their desired trajectories. For trolley's exact positioning the trolley dynamics is identified via an error equation identifier structure. The second stage is a nonlinear residual sway control that starts at the end of first stage. The control design for the second stage is investigated from the perspective of controling an underactuated system, and the control law combines the feedback linearization and variable structure control.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.197-202
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• 1991

Recently, an output feedback variable structure control scheme(OFVSCS) is proposed to remove the assumption of full state availability and to make the application of VSC scheme to the high order systems with unmeasurable state variables possible. In this paper, a design method of an output feedback variable structure control system (IOFVSCS) that guarantees the invariance of the sliding mode against process parameter variation and external disturbance is proposed. The IOFVSCS is composed of two components; dynamic switching surface driven by measured I/0 informations and switching control input generator driven by switching surface information and measured output, where the two components are constructed by adopting unknown vector modelling approach. The invariance condition for the IOFVSCS is proved to be the same as that of the conventional VSCS. Simulation results show that the IOFVSCS can be designed to have robust properties better than that of the conventional VSCS in spite that the IOFVSCS is driven by small amount of measured information.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1021-1024
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• 1999

In this paper, we propose a modified feedback neural network structure for adaptive control of robot manipulators. The proposed structure is that all of network output feedback into hidden units and output units. Learning algorithm is standard back-propagation algorithm. The simulation showed the effectiveness of using the new neural network structure in the adaptive control of robot manipulators.

• Smart Structures and Systems
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• v.1 no.3
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• pp.283-308
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• 2005

This paper features about the modeling and design of a fast output sampling feedback controller for a smart Timoshenko beam system for a SISO case by considering the first 3 vibratory modes. The beam structure is modeled in state space form using FEM technique and the Timoshenko beam theory by dividing the beam into 4 finite elements and placing the piezoelectric sensor/actuator at one location as a collocated pair, i.e., as surface mounted sensor/actuator, say, at FE position 2. State space models are developed for various aspect ratios by considering the shear effects and the axial displacements. The effects of changing the aspect ratio on the master structure is observed and the performance of the designed FOS controller on the beam system is evaluated for vibration control.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.411-416
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• 2010

In this paper, an integral variable structure static output feedback controller with an integral-augmented sliding surface is designed for the improved robust control of a uncertain system under unmatched system uncertainty and matched input matrix uncertainty and disturbance satisfying some conditions. To effectively remove the reaching phase problems, an output dependent integral augmented sliding surface is proposed. Its equivalent control and ideal sliding mode dynamics are obtained. The previous some limitations is overcome in this systematic design. A stabilizing control with the closed loop exponential stability is designed for all unmatched system matrix uncertainties and proved together with the existence condition of the sliding mode on S=0. To show the usefulness of the algorithm, a design example and computer simulations are presented.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.474-478
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• 1998

A new approach for the decision feedback equalizer(DFE) based on the back-propagation neural networks is described. We propose the method of optimal structure for back-propagation neural networks model. In order to construct an the optimal structure, we first prescribe the bounds of learning procedure, and the, we employ the method of incrementing the number of input neuron by utilizing the derivative of the error with respect to an hidden neuron weights. The structure is applied to the problem of adaptive equalization in the presence of inter symbol interference(ISI), additive white Gaussian noise. From the simulation results, it is observed that the performance of the propose neural networks based decision feedback equalizer outperforms the other two in terms of bit-error rate(BER) and attainable MSE level over a signal ratio and channel nonlinearities.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.734-738
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• 1996

An adaptive output feedback controller is designed for tracking control of an n-link robot manipulator with unknown load. High-gain observers with same structure as error dynamic systems are used to estimate joint velocities. The parameter adaptation is achieved by the smoothed projection algorithm. The control inputs are saturated outside a domain of interest. Simulation results on a 2-link manipulator illustrate that when the speed of the high-gain observer is sufficiently high, the proposed controller recovers the performance under state feedback control.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.443-443
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• 2000

A systematic scheme is developed fer the design of new adaptive feedback linearizing controllers for nonlinear systems. The developed adaptation law estimates the uncertain time-varying parameters using the structure of diffeomorphisrn. Our scheme is applicable to a class of nonlinear systems which violates the restrictive parametric-pure-feedback condition [4]-[6].

• Smart Structures and Systems
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• v.18 no.6
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• pp.1251-1267
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• 2016

In this paper, Simple Adaptive Control (SAC) is used to enhance the seismic response of nonlinear tall buildings based on acceleration feedback. Semi-active MR dampers are employed as control actuator due to their reliability and well-known dynamic models. Acceleration feedback is used because of availability, cost-efficiency and reliable measurements of acceleration sensors. However, using acceleration feedback in the control loop causes the structure not to apparently meet some requirements of the SAC algorithm. In addition to defining an appropriate SAC reference model and using inherently stable MR dampers, a modification in the original structure of the SAC is proposed in order to improve its adaptability to the situation in which the plant does not satisfy the algorithm's stability requirements. To investigate the performance of the developed control system, a numerical study is conducted on the benchmark 20-story nonlinear building and the responses of the SAC-controlled structure are compared to an $H_2/LQG$ clipped-optimal controller under the effect of different seismic excitations. As indicated by the results, SAC controller effectively reduces the story drifts and hence the seismically-induced damage throughout the structural members despite its simplicity, independence of structural parameters and while using fewer number of dampers in contrast with the $H_2/LQG$ clipped-optimal controller.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.12
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• pp.1289-1295
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• 2012

An adaptive digital predistortion (PD) technique is proposed for linearization of power amplifiers (PAs) in multiple-input multiple-output (MIMO) transmitters. We consider a PD structure equipped with only one combined feedback path while conventional systems have multiple feedback paths. Hence, the proposed structure is much simpler than that of multiple feedback paths. Based on the structure, a new PD algorithm is derived. The simulation results show that linearization performance of the proposed method is almost the same as the conventional multiple feedback technique while the former is much simpler to implement than the latter.