• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.302-305
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• 1999

Twin-servo mechanism is used to increase the payload capacity and speed of high precision motion control system. In this paper, we propose a robust synchronizing motion control algorithm to cancel out the skew motion of twin-servo system caused by different dynamic characteristics of two driving systems and the vibration generated by high accelerating and decelerating motions. This proposed control algorithm consists of separate feedback motion control algorithm of each driving system and skew motion compensation algorithm between two systems. Robust model reference tracking controller is proposed as a separate motion controller and its disturbance attenuation property is shown. For the synchronizing motion, skew motion compensation algorithm is designed, and the stability of whole Closed loop system is proved based on passivity theory.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.858-863
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• 1990

A synthesis of feedback control-law with combined H$_{2}$/H$_{\infty}$ perfoemance criteria is proposed for discrete-time systems, under the assumption that the state is available for feedback. An auxiliary minimization problem is defined to enforce the H$_{\infty}$ disturbance attenuation constrain while minimizing the H$_{2}$ performance bound. The design equation is presented in terms of a modified Riccati equation which leads to the standard LQ solution when the H$_{\infty}$ constraint is completely relaxed. The results of the paper clarity the correspondences between H$_{2}$/H$_{\infty}$ results in discrete-time systems and their continuous-time counter-parts.rts.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1260-1265
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• 2008

This study deals with modeling and flight control of quadrotor type (QRT) unmanned aerial vehicles (UAVs). Rigorous dynamic model of a QRT UAV is obtained both in reference and body frame coordinate systems. A disturbance observer (DOB) based controller using the derived dynamic models is also proposed for robust hovering control. The control input induced by DOB is helpful to use simple equations of motion satisfying accurate derived dynamics. The experimental results show the performance of the proposed control algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.2
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• pp.389-395
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• 2007

In this paper, we consider the robust non-fragile $H_{\infty}$ output feedback controller design method for uncertain descriptor systems with feedback and observer gain variations. The existence condition of observer-based robust and non-fragile $H_{\infty}$ output feedback controller and the controller design method are Presented on the basis of linear matrix inequality approach. The proposed robust non-fragile $H_{\infty}$ output feedback controller guarantees asymptotic stability, non-fragility, $H_{\infty}$ norm bound within a prescribed level in spite of disturbance, parameter uncertainty, and feedback/observer gain variations.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1999.11a
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• pp.121-126
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• 1999

In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. In order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. Also, the learning architecture to compute inverse kinematic coordinates transformations in the manipulator of auto-equipment systems is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulations are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.4
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• pp.295-303
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• 2001

The demand for steam generators is increasing in industrial systems in which the design strategy should be implemented for safe and efficient operation of steam generators. It is, however, difficult to design a controller by the conventional method because of the nonlinear dynamics of the steam generator and influences by the set value of disturbance. This paper presents an automatic parameter optimization technique for a multivariable fuzzy controller using evolutionary strategy, At first, we use the steady state information such as a steady state gain matrix(SSGM) and a relative gain matrix(RGM). We can obtain much information on the control inputs and the outputs of the boiler-turbine system from the matrices. In order to determine the structure of the controller by using RGM and SSGM, the fuzzy rules are trained by evolutionary strategy. The good performance of the proposed multivariable fuzzy controller is verified through simulations.

• Journal of Electrical Engineering and Technology
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• v.7 no.1
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• pp.34-45
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• 2012

In the present paper, the mover speed control of a linear induction motor (LIM) using a sliding mode control design is proposed, considering the end effects. First, the indirect field-oriented control LIM is derived, considering the end effects. The sliding mode control design is then investigated to achieve speed- and flux-tracking under load thrust force disturbance. The numerical simulation results of the proposed scheme present good performances in comparison to that of the classical sliding mode control.

• The KIPS Transactions:PartA
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• v.8A no.1
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• pp.56-62
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• 2001

This paper presents a neural network based variable structure control scheme for nonlinear systems. In this scheme, a set of local variable structure control laws are designed on the basis of the linear models about preselected representative points which cover the range of the system operation of interest. From the combination of the set of local variable structure control laws, neural networks infer the approximate control input in between the operating points. The neural network based variable structure control alleviates the effects of model uncertainties, which cannot be compensated by the control techniques using feedback linearization. It also relaxes the discontinuity in the system’s behavior that appears when the control schemes based on the family of the linear models are applied to nonlinear systems. Simulation results of a ball and beam system, to which feedback linearization cannot be applied, demonstrate the feasibility of the proposed method.

• Smart Structures and Systems
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• v.25 no.1
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• pp.37-45
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• 2020

This paper aims at enlightening the properties, computational and implementation issues related to Kalman filter based state estimation algorithms and sliding mode observers, by applying them for estimating the states of a smart structure system. The Kalman based estimators considered in this work are Kalman filter and information filter and, the sliding mode observers considered are Utkin observer and higher order sliding mode observer. A fourth order linear time invariant model of a piezo actuated beam is used in this work. This structure is embedded with four number of piezo patches, of which two act as sensors, one as disturbance actuator and the other as control actuator. The performance of the state estimation algorithms is evaluated through simulation, for the first two vibrating modes of the piezo actuated structure, when the structure is maintained at first mode and second mode resonance.

• ETRI Journal
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• v.29 no.5
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• pp.596-606
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• 2007

It is well known that an OFDM receiver is vulnerable to synchronization errors. Despite fine estimations used in the initial acquisition, there are still residual synchronization errors. Though these errors are very small, they severely degrade the bit error rate (BER) performance. In this paper, we propose a residual error elimination scheme for the digital OFDM baseband receiver aiming to improve the overall BER performance. Three improvements on existing schemes are made: a pilot-aided recursive algorithm for joint estimation of the residual carrier frequency and sampling time offsets; a delay-based timing error correction technique, which smoothly adjusts the incoming data stream without resampling disturbance; and a decision-directed channel gain update algorithm based on recursive least-squares criterion, which offers faster convergence and smaller error than the least-mean-squares algorithms. Simulation results show that the proposed scheme works well in the multipath channel, and its performance is close to that of an OFDM system with perfect synchronization parameters.