• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.11
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• pp.69-75
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• 2009

In this paper, a self-tuning regulator for a speed control of a permanent magnet type stepping motor is proposed. The self-tuning theory provides a nonlinear modeling of a stepping motor drive system and can provide the controller with information regarding the reference variation and parameter variation of the stepping motor through the on-line estimation. The proposed self-tuning regulator organize the positive feedback loop and IP(Integral-Proportional) type. Therefore, the proposed self-tuning regulator has a robust control capabilities during dynamic operation. The availability of the proposed controller is verified through experimental results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.518-526
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• 2008

The fuzzy self-tuning PID controller is a PID controller with a fuzzy logic mechanism for tuning its gains on-line. In this structure, the proportional, integral and derivative gains are tuned on-line with respect to the change of the output of system under control. This paper deals with two types of fuzzy self-tuning PID controllers, rule-based fuzzy PID controller and learning fuzzy PID controller. As a medical application of fuzzy PID controller, the proposed controllers were implemented and evaluated in a laparoscopic surgery robot system. The proposed fuzzy PID structures maintain similar performance as conventional PID controller, and enhance the position tracking performance over wide range of varying input. For precise approximation, the fuzzy PID controller was realized using the linear reasoning method, a type of product-sum-gravity method. The proposed controllers were compared with conventional PID controller without fuzzy gain tuning and was proved to have better performance in the experiment.

• Structural Engineering and Mechanics
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• v.87 no.3
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• pp.231-242
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• 2023

A novel combinatorial type-2 adaptive neuro-fuzzy inference system (T2-ANFIS) and robust proportional integral derivative (PID) control framework for intelligent vibration mitigation of uncertain structural system is introduced. The fuzzy logic controllers (FLCs), are designed independently of the mathematical model of the system. The type-1 FLCs, have a limited ability to reduce the effect of uncertainty, due to their fuzzy sets with a crisp degree of membership. In real applications, the consequent part of the fuzzy rules is uncertain. The type-2 FLCs, are robust to the fuzzy rules and the process parameters due to the fuzzy degree of membership functions and footprint of uncertainty (FOU). The adaptivity of the proposed method is provided with the optimum tuning of the parameters using the neural network training algorithms. In our approach, the PID control force is obtained using the generalized type-2 neuro-fuzzy in such a way that the stability and robustness of the controller are guaranteed. The robust performance and stability of the presented framework are demonstrated in a numerical study for an eleven-story seismically-excited building structure combined with an active tuned mass damper (ATMD). The results indicate that the introduced type-2 neuro-fuzzy PID control scheme is effective to attenuate plant states in the presence of the structured and unstructured uncertainties, compared to the conventional, type-1 FLC, type-2 FLC, and type-1 neuro-fuzzy PID controllers.

• The Transactions of the Korean Institute of Power Electronics
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• v.17 no.3
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• pp.259-265
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• 2012

This paper introduces a method for design and implementation of a current controller for boost converter operating in continuous conduction mode (CCM) using a digital signal processor (DSP). A Proportional-Integral (PI) type current controller outputs an average voltage command for inductor, used in the input side of the boost converter, and the duty-ratio of PWM (pulse width modulation) signal for switching device is directly calculated from the average voltage command. The gains of the PI current controller are selected such that the current response characteristics are the same as those of a first-order low-pass filter. The proposed current control scheme is implemented using a DSP based on fixed-point math operations and an experimental study has been performed to validate the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.3
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• pp.188-196
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• 1987

This paper describes a design method for the buck type switching regulator to improve transient and steady state performances. Necessary design considerations on the power stage are given before designing the controller to obtain better trandient responses with less control effort and a feedforward compensation is also given to effectively improve the steady state performance. In the design of the controller, a PIM (proportional-integral-measurable) control method with optimized constant feedback gains is presented to get better tansient and steady state performances without complicating the implementation of controller. Computer simulations and experimental results are given to show the usefulness of the presented technique.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.897-899
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• 1999

This paper presents a scaling factor tuning method to improve the performance of fuzzy logic controller. Tuning rules and reasoning are utilized off-line to determine the scaling factors based on absolute value of the error and its difference. In this paper We proposed a new method to generate fuzzy logic controllers throught genetic algorithm. The developed approach is subsequently applied to the design of proportional plus integral type fuzzy controller for a dc-servo motor control system. The performance of this control system is demonstrated higher than a conventional fuzzy logic controller(FLC).

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.445-445
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• 2000

This paper proposes the MIMO indirect adaptive fuzzy controller to control the two-link manipulators. The input-output linearization technique, equivalent control input plus integral term, augmented error model and recursive least square adaptive law are used fer the controller. The linear type of fuzzifier-defuzzifier fuzzy logic system used for nonlinear function makes easy to farm the error model and able to follow the adaptive system approach. Such that control approach, the control system is not required joint speed and accerelation measurement and easy to implement and tune. The simulation results showed that the proposed controller has good control performance, stability, very small tracking error, decoupling, fast convergence, robust to parameter variation and load.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.509-511
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• 1998

Design of fuzzy logic controllers encounters difficulties in the selection of optimized membership function and fuzzy rule base, which is traditionally achieved by tedious trial-and-error process. In this paper We proposed a new method to generate fuzzy logic controllers throught genetic algorithm(GA). The controller design space is coded in base-7 strings chromosomes, where each bit gene matches the 7 discrete fuzzy value. The developed approach is subsequently applied to the design of proportional plus integral type fuzzy controller for a do-servo motor control system. It was presented in discrete fuzzy linguistic value, and used a membership function with Gaussian curve. The performance of this control system is demonstrated higher than that of a conventional PID controller and fuzzy logic controller(FLC).

• Journal of Advanced Navigation Technology
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• v.20 no.3
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• pp.226-231
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• 2016

Researching and developing mobile robot are quite important. Autonomous driving of mobile robot is important in various working environment. For its autonomous driving, mobile robot detects obstacles and avoids them. Purpose of this thesis is to analyze kinematics model of the mobile robot and show the efficiency of type-2 fuzzy self-tuning PID controller used for controling steering angle. Type-2 fuzzy is more flexible in verbal expression than type-1 fuzzy because it has multiple values unlike previous one. To compare these two controllers, this paper conduct a simulation by using MATLAB Simulink. The result shows the capability of type-2 fuzzy self-tuning PID is effective.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.781-786
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• 1998

Since a gas turbine is still a significant contributor to peak time, it is very important to tune the gains of P. I. D to get a maximum power and stability within permissible limits. In the gas turbine, the main control loop must adjust the fuel flow to ensure the correct output power and frequency. but it is not easy, because the control loop is composed of many subsystems. In this paper we acquire a transfer function based on the operations data of Gun-san gas turbine and study to apply a loop compensation type 2-DOF PID controller tuning by neural-network to control loop of gas turbine to reduce phenomena caused by integral and derivative actions through simulation. We obtained satisfactory results to disturbances of subcontrol loop such as, fuel flow, air flow, turbine extraction temperature.