• Journal of the Korean Society for Precision Engineering
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• v.21 no.10
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• pp.65-77
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• 2004

In this paper, we propose an adaptive mechanism based on immune algorithm and neural network identifier technique. It is also applied fur an autonomous guided vehicle (AGV) system. When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged due to the abrupt change of PID parameters since the parameters are almost adjusted randomly. To solve this problem, we use the neural network identifier (NNI) technique fur modeling the plant and humoral immune algorithm (HIA) which performs the parameter tuning of the considered model, respectively. After the PID parameters are determined in this off-line manner, these gains are then applied to the plant for the on-line control using an immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough initially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. Finally, the simulation and experimental result fur the control of steering and speed of AGV system illustrate the validity of the proposed control scheme. These results for the proposed method also show that it has better performance than other conventional controller design methods.

• Journal of the Optical Society of Korea
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• v.20 no.4
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• pp.470-475
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• 2016

In this paper, the effects of filter parameters on a supercontinuum-based all-optical thresholder are experimentally investigated. By tuning the filter parameters, the power transfer function and power transmission function are tailored. The experimental results show that a thresholder with short center wavelength has a better power function, and the slope in the middle level of the thresholder increases with increasing bandwidth. Through tuning the filter parameters, the thresholder can achieve a steplike power transfer function for optical thresholding, and a steplike power transmission function for optical self-switching. This makes the supercontinuum-based thresholder more flexible, and allows customization of performance to meet different demands in various applications.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.4
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• pp.201-212
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• 2000

In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied for the autonomous guided vehicle(AGV) driving. When the immune algorithm is applied to the PID controller, there exists the cast that the plant is damaged due to the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network is used to model the plant and the parameter tuning of the model is performed by the immune algorithm. After the PID parameters are determined in this off-line manner, these gains are then applied to the plant for the on-line control using immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough intially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. The computer simulation for the control of steering and speed of AGV is performed. The results show that the proposed controller has better performances than other conventional controllers.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.6
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• pp.555-562
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• 1997

With only the classical PID controller applied to control of a DC motor, 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 known exactly. However, in case when some of system parameters or operating conditions are uncertain or unknown, the fixed PID controller does not guarantee good performance, which is assumed with precisely known system parameters and operating conditions. In view of this and the 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 world wide asymptotically. Computer simulation and experimental results are given to demonstrate the effectiveness of the proposed PID learning controller, thereby showing its superiority to the conventional fixed PID controller.

• Geomechanics and Engineering
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• v.16 no.5
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• pp.527-538
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• 2018

The study on the performance of the nonlinear friction tuned mass dampers (FTMD) for the mitigation of the seismic responses of the structures is a topic that still inspires the efforts of researchers. The present paper aims to carry out a numerical study on the optimum tuning of TMD and FTMD parameters using a multi-objective particle swarm optimization (MOPSO) algorithm including soil-structure interaction (SSI) effects for seismic applications. Considering a 3-story structure, the performances of the optimized TMD and FTMD are compared with the uncontrolled structure for three types of soils and the fixed base state. The simulation results indicate that, unlike TMDs, optimum tuning of FTMD parameters for a large preselected mass ratio may not provide a best and optimum design. For low mass ratios, optimal selection of friction coefficient has an important key to enhance the performance of FTMDs. Consequently, a free parameter search of all FTMD parameters provides a better performance in comparison with considering a preselected mass ratio for FTMD in the optimum design stage of the FTMD. Furthermore, the SSI significant effects on the optimum design of the TMD and FTMD. The simulation results also show that the FTMD provides a better performance in reducing the maximum top floor displacement and acceleration of the building in different soil types. Moreover, the performance of the TMD and FTMD decrease with increasing soil softness, so that ignoring the SSI effects in the design process may give an incorrect and unrealistic estimation of their performance.

• Proceedings of the KIEE Conference
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• 2002.07b
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• pp.1165-1168
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• 2002

This paper presents a neural network based self-tuning fuzzy PID control scheme with variable learning rate for sensorless vector controlled induction motor drives. MRAS(Model Reference Adaptive System) is used for rotor speed estimation. When induction motor is continuously used long time. its electrical and mechanical parameters will change, which degrade the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, introduces a single neuron with a back-propagation learning algorithm to tune the control parameters, and proposes a variable learning rate to improve the control performance. The proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink and the experiment using DS1102 board show the robustness of the proposed controller to parameter variations.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.1
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• pp.17-24
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• 2004

This paper proposes an optimal robust LQ-PID controller design method for the second order systems to satisfy the design specifications in time domain. The tuning parameters of LQ-PID controller are determinated by the relationships between the design parameters of the overshoot and the settling time which are design specifications in time domain, and the weighting factors Q and R in LQR. we can achieve the performance-robustness in time domain as well as the stability-robustness.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.378-384
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• 1993

In this paper, an intelligent adaptive controller is proposed for the process with unmodelled dynamics. The intelligent adaptive controller consists of the numeric adaptive controller and the intelligent tuning part. The continuous scheme is used for the numeric adaptive controller to avoid the problems occurred in the discrete time schemes. The adaptive controller is adopted to the process with time delay. It is an implicit adaptive algorithm based on GMV using the emulator. The tuning part changes the design parameters in the control algorithm. It is a multilayer neural network trained by robustness analysis data. The proposed method can improve the robustness of the adaptive control system because the design parameters are tuned according to the operating points of the process. Through the simulation, robustnesses are shown for intelligent adaptive controller. Finally, the proposed algorithms are implemented on the electric furnace temperature control system. The effectiveness of the proposed algorithm is shown from experiments.

• Proceedings of the KAIS Fall Conference
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• 2010.11a
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• pp.341-343
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• 2010

The parameters of PID controller should be readjusted whenever system character change. In spite of a rapid development of control theory, this work needs much time and effort of expert. In this paper, to resolve this defect, after the sample of parameters in the changeable limits of system character is obtained, these parametrs are used as desired values of back propagation learning algorithm, also neural network auto tuner for PID controller is proposed by determing the optimum structure of neural network. Simulation results demonstrate that auto-tuning proper to system character can work well.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.9
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• pp.1741-1747
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• 2011

Though various techniques have been studied as a way of adjusting parameters of PID controllers, no perfect method of determining parameters is available to date. This paper proposes a new method for enhancing performance of PID controllers by using the characteristics of dual-input describing function (DIDF). In other words, if nonlinear elements with two inputs (DIDF) are connected in series to the plant, the critical point (-1+j0) for Nyquist stability theory can be moved to a position arbitrarily selected on the complex plane by determining necessary coefficients of the DIDF appropriately. This makes the application of the existing conventional PID parameter tuning methods a lot easier, and stability and robustness of the system are improved simultaneously due to the DIDF inserted.