• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.402-406
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• 1989

Since the control parameters of classical PID controller are fixed for all control period, it is not easy to produce a desired transition phenomena. We incorporate an iterative learning scheme to the linear controller so that it has more flexibility and adaptation capability especially in the transition period. In this paper a hybrid type learning controller is proposed in which fixed linear controller guides learning at the beginning stage. Once a perfect learning is achieved, then the control action is performed by only the learning controller. A computer simulation result demonstrates better performance during transition time than that with only linear PD controller.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.485-492
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• 1990

This paper propose a optimal design method for robust notion controllers of under-water vehicles using the combined technique between classical and modern theories. The proposed method is presented which utilizes classical control methods to obtain a good robustness and modern control methods to set optimal gains. LQ, SVD, multivariable frequency analysis and Bode-Root Locus (BRL) plot are used.

• Journal of Electrical Engineering and Technology
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• v.11 no.3
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• pp.662-669
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• 2016

In this work, nonlinear control of a three-phase shunt active power filter (SAPF) has been studied and compared to classical control based on proportional integral regulator. The control strategy is based on the direct current method using sliding mode control (SMC), where the aim is to regulate the average voltage across the dc bus of the inverter. Details are given for the control algorithm; the controller is comprised of a current loop which utilizes a hysteresis controller to generate the gating signals for the switching devices, and a nonlinear controller based on SMC law which is different from classical laws based on error between reference and measured output voltage of the inverter. Sliding surface applied in this work contains the whole of state variables, in order to ensure full control of the system behavior in the presence of disturbances that affect the supply source, the load parameters or the reference value. The designed controller offers advantage that it can gives the improvement of dynamic and static performances in cases of large disturbances. A comparison of the effects of PI control and SMC on the APF response in steady stat, under line variations, load variations, and different component variations is performed.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.333-338
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• 1993

A feedback linearization controller for EMS system is implemented using DSP. In this paper, we show that given EMS system is input-state linearizable and satisfies some robustness condition. Also we derive feedback linearization controller for given system. Finally, some experiments are performed to demonstrate the performance of the proposed controller-especially, comparing this with the classical state feedback controller using linear perturbation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.85-88
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• 2000

In this paper, the ultra precision positioning system for piezoelectric actuator using hysteresis compensation has been developed. Piezoelectric actuators exhibit limited accuracy in tracking control due to their hysteresis nonlinearity. The main purpose of the proposed controller is to compensate the hysteresis nonlinearity of the piezoelectric actuator. The controller is composed of a PD, hysteresis compensation and neural network part in parallel manner, at first, the excellent tracking performance of the neural network controller was verified by experiments and was compared with the classical PD controller.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.3
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• pp.143-148
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• 2018

In this paper, the optimal fuzzy logic controller(FLC) for a hybrid renewable energy system(HRES) is proposed. Generally, hybrid renewable energy systems can consist of wind power, solar power, fuel cells and storage devices. The proposed FLC can effectively control the entire HRES by determining the output power of the fuel cell or the absorption power of the electrolyzer. In general, fuzzy logic controllers can be optimized by classical optimization algorithms such as genetic algorithms(GA) or particle swarm optimization(PSO). However, these FLC have a disadvantage in that their performance varies greatly depending on the control parameters of the optimization algorithms. Therefore, we propose a method to optimize the fuzzy logic controller using the teaching-learning based optimization(TLBO) algorithm which does not have the control parameters of the algorithm. The TLBO algorithm is an optimization algorithm that mimics the knowledge transfer mechanism in a class. To verify the performance of the proposed algorithm, we modeled the hybrid system using Matlab Tool and compare and analyze the performance with other classical optimization algorithms. The simulation results show that the proposed method shows better performance than the other methods.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.9-15
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• 1999

A piezoelectric actuator is widely used in precision positioning applications due to its excellent positioning resolution. However, the piezoelectric actuator lacks in repeatability because of its inherently high hysteresis characteristic between voltage and displacement. In this paper, a controller is proposed to compensate the hysteresis nonlinearity. The controller is composed of a PID and a neural network part in parallel manner. The output of the PID controller is used to teach the neural network controller by the unsupervised learning method. In addition, the PID controller stabilizes the piezoelectric actuator in the beginning of the learning process, when the neural network controller is not learned. However, after the learning process the piezoelectric actuator is mainly controlled by the neural netwok controller. In this paper, the excellent tracking performance of the proposed controller was verified by experiments and was compared with the classical PID controller.

• Proceedings of the KIPE Conference
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• 2014.11a
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• pp.203-204
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• 2014

In this paper, single-phase uninterruptible power supply design method is presented. In this control scheme, input current, output current and output voltage are used. For voltage control PR controller is used and that for current controller is PI controller. The gains for controllers are sought by the classical method for determining gains. Throughout simulations the performance of single-phase UPS is verified.

• Journal of Power Electronics
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• v.8 no.3
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• pp.268-274
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• 2008

This paper presents a proposed position controller for a vector controlled induction motor. The position controller design depends on the rotational motion equations and a classical speed controller (CSC) performance. The CSC is designed to have the ability to track variable reference inputs and to provide a predefined system performance. Standard position controller in industry is presented to analyze its performance and its drawbacks. Then the proposed position controller is designed, based on the well defined rotational motion equations. The proposed position controller and the CSC are applied to control the position and speed of the vector controlled induction motor with different ratings. Simulation results at different operating conditions are presented to evaluate the proposed controllers' performance. The results show that the CSC can drive the motor with a predefined speed performance and can track a variable reference speed with an approximately zero steady state error. The results also show that the proposed position controller has the ability to effect high-precision positioning in a limited time and to track a variable reference position with a zero steady state error.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.347-350
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• 1996

With only the classical PID controller applied to control of a DC motor, a good (target) performance characteristic of the controller can be obtained, if all the model parameters of DC motor and operating conditions such as external load torque, disturbance, etc. are exactly known. However, in case when some of system parameters or operating conditions are uncertain or unknown, the fixed PID controller does not guarantee the good performance which is assumed with precisely known system parameters and operating conditions. In view of this and robustness enhancement of DC motor control system, we propose a PID learning controller which consists of a set of learning rules for PID gain tuning and learning of an auxiliary input. The proposed PID learning controller is shown to drive the state of uncertain DC motor system with unknown system parameters and external load torque to the desired one globally asymptotically. Computer simulation results are given to demonstrate the effectiveness of the proposed PID learning controller, thereby showing whose superiority to the conventional fixed PID controller.