• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.234-234
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• 2000

A direct torque control(DTC) based sensorless speed control system which employs a new closed loop flux observer is proposed. The flux observer takes an adaptive scheduling gains where motet speed is used as the scheduling variable. Adaptive nature comes from the fact that the estimated values of stator resistance and speed are included as observer parameters. The parameters of the PI controllers adopted in the adaptive law for the estimation of stator resistance and motor speed are determined by simple genetic algorithm. Simulation results in low speed region are given for comparison between proposed and conventional flux estimate scheme.

• Journal of Power System Engineering
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• v.9 no.4
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• pp.175-180
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• 2005

Incorrections between model and plant are parameter, system order uncertainties and modeling error due to disturbance like friction. Therefore to achieve a good tracking performance, adaptive discrete time sliding mode tracking controller is used under time-varying desired position. Based on the diophantine equation, a new discrete time sliding function is defined and utilized for the control law. Robustness is increased by using both a recursive least-square method and a sliding function-based nonlinear feedback. The effectiveness of the proposed control algorithm is proved by the results of simulation and experiment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.690-694
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• 1996

In order to regulate the cutting force at a desired level during peripheral end milling processes, a feedrate override Adaptive Control Constant system was developed. This paper presents an explicit pole-assignment PI-control law through spindle motor current monitoring and its application to cutting force regulation for feedrate optimization. An experimental set-up is constructed for the commercial CNC machining center without any major changes of the structure. A data transfer system is constructed with standard interface between an IBM compatible PC and a CNC of the machining center. Experimental results show the validity of the system.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.1-12
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• 2022

In order to overcome the power fluctuation issues in photovoltaic (PV) smart grid-connected systems and the inverter nonlinearity model problem, an adaptive backstepping command-filter and a double second order generalized Integrators (DSOGI) controller are designed in order to tune the AC current and the DC-link voltage from the DC side. Firstly, we propose to present the filter mathematical model throughout the PV system, at that juncture the backstepping control law is applied in order to control it, Moreover the command filter is bounded to the controller aiming to exclude the backstepping controller differential increase. Additionally, The adaptive law uses Lyapunov stability criterion. Its task is to estimate the uncertain parameters in the smart grid-connected inverter. A DSOGI is added to stabilize the grid currents and eliminate undesirable harmonics meanwhile feeding maximum power generated from PV to the point of common coupling (PCC). Then, guaranteeing a dynamic effective response even under very unbalanced loads and/or intermittent climate changes. Finally, the simulation results will be established using MATLAB/SIMULINK proving that the presented approach can control surely the smart grid-connected system.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1981-1986
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• 2004

In this study, the basic motor control system had been investigated. The controller for this study consists of two main parts, a feedforward controller part and a feedback controller part. Each part will deals with different control problems. The feedback controller deals with robustness and stability, while the feedforward controller deals with response speed. The feedforward controller, used to solve the tracking control problem, is adaptable. To make such a tracking perfect, an adaptive law based on Feedback Error Learning (FEL) is designed so that the feedforward controller becomes an inverse system of the controlled plant. The novelty of FEL method lies in its use of feedback error as a teaching signal for learning the inverse model. The theory in $H^{\infty}$-Control is selected to be applied in the feedback part to guarantee the stability and solve the robust stabilization problems. The simulation of each individual part and the integrated one are taken to clarify the study.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.1169-1172
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• 1995

In this paper, the external force control and hybrid force control algorithms are proposed to apply Deburring process. the purpose of adjust which can be implemented to on unknown environments, adaptive control law(MRAC) is adopted. IF a model system is given, the plant system can be controlled on the way which we will introduce to. We showed the validation and the possibility of Deburring process with multi-dimensional force control through experiments. the experimental result show the validity of Deburring in the robot manipulator.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.586-588
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• 2011

Adaptive feedforward control(AFC) is largely aimed for improving control performance of dynamic systems particularly involving periodic disturbance signals in engineering fields. This paper presents a novel hybrid AFC approach for specific systems with multiple disturbances in control input and state variables. The proposed AFC mechanism is hierarchically composed of the conventional AFC and a PID typed auxiliary control law in parallel. The former is generic to decrease periodic disturbance in control actuators and the latter is additionally constructed to overcome control deterioration due to time-varying uncertainty of given systems. We carry out numerical simulation to test reliability of the hybrid AFC system and compare its control performance with a well-known conventional AFC method in terms of time and frequency domains for proving of its superiority.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.838-847
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• 1994

Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.2
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• pp.112-124
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• 2004

A neural network based adaptive reconfigurable flight controller is presented for a class of discrete-time nonlinear flight systems in the presence of variations of aerodynamic coefficients and control effectiveness decrease caused by control surface damage. The proposed adaptive nonlinear controller is developed making use of the backstepping technique for the angle of attack, sideslip angle, and bank angle command following without two time separation assumption. Feedforward multilayer neural networks are implemented to guarantee reconfigurability for control surface damage as well as robustness to the aerodynamic uncertainties. The main feature of the proposed controller is that the adaptive controller is developed under the assumption that all of the nonlinear functions of the discrete-time flight system are not known accurately, whereas most previous works on flight system applications even in continuous time assume that only the nonlinear functions of fast dynamics are unknown. Neural networks learn through the recursive weight update rules that are derived from the discrete-time version of Lyapunov control theory. The boundness of the error states and neural networks weight estimation errors is also investigated by the discrete-time Lyapunov derivatives analysis. To show the effectiveness of the proposed control law, the approach is i]lustrated by applying to the nonlinear dynamic model of the high performance aircraft.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.8
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• pp.535-542
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• 2016

In this study, a model reference adaptive controller is developed to regulate the outlet air flow rate of centrifugal compressor for automotive supercharger. The centrifugal compressor is developed using the analytical based method to predict the transient behavior of operating and the designed model is validated with experimental data to confirm the system accuracy. The model reference adaptive control structure consists of a compressor model and a MRAC(model reference adaptive control) mechanism. The feedback control do not robust with variation of system parameter but the applied adaptive control is robust even if the system parameter is changed. As a result, the MRAC was regulated to reference air flow rate. Also MRAC was found to be more robust control compared with the feedback control even if the system parameter is changed.