• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.1182-1184
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• 2001

This paper presents a neural network based self-tuning fuzzy PID control scheme for induction motor speed control. The PID controller is being widely used in industrial applications. When continuously used long time, the electric and mechanical parameters of induction motor change, degrading the performance of PID controller considerably. This paper re-analyzes the fuzzy controller as conventional PID controller structure, and proposes a neural network based self-tuning fuzzy PID controller whose scaling factors are adjusted automatically. Proposed scheme is simple in structure and computational burden is small. The simulation using Matlab/Simulink is performed to verify the effectiveness of the proposed scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.1
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• pp.19-26
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• 2005

This paper is concerned with the design of a predictive PID controller which has similar features to the model-based predictive controller. A PID type control structure is defined, which includes prediction of the outputs and the recalculation of new set points using the future set point data. The optimal values of the PID gains are precalculated using the values of gains calculated from an unconstrained generalized predictive control algorithm. Simulation studies demonstrate the performance of the proposed controller and the results are compared with the conventional PID and fuzzy control algorithms.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.791-793
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• 1999

In this paper, neural network-supervisory control method is proposed to minimize the effect of system uncertainty by load change and disturbance in the PID control system. In the proposed method, PID controller performs main control action by performing control within constraint error. And neural network-supervisory controller performs control action when error reaches the boundary of constraint error. Combining neural network-supervisory controller to guarantee the stability into PID control system, the resulting PID control system is expected to show better performance in the system with load change and disturbance. Simulation applying PID controller and neural network-supervisory controller showed excellence of proposed method.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.782-784
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• 1999

This paper deals with speed control of DC-servo motor using a Back-Propagation(BP) Learning Algorism and a PID controller Conventionally in the industrial control, PID controller has been used. But the PID controller produced suitable parameter of each system and also variable of PID controller should be changed enviroment, disturbance, load. So this paper revealed for experimental, a neural network and a PID controller combined system using developed speed characters of a Variable Load DC-servo motor. The parameters of the plant are determined by neural network perform on on-line system after training the neural network on off-line system.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.628-631
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• 2005

In this paper, we propose tuning method of PID-PD controller to satisfy design specifications in frequency domain as well as time domain. The proposed tuning method of PID-PD controller that consist of the convex set of PID and PI-PD controller controls the closed-loop response to locate between the step responses, and Bode magnitudes of closed-loop transfer functions controlled by PID and PI-PD controller. The controller is designed by the optimum tuning method to minimize the proposed specific cost function subject to sensor noise insensitivity and robust stability. Its effectiveness is examined by the case study and analysis.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.5
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• pp.373-384
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• 2002

This paper presents a robust PID controller design technique using the concept of model matching method in the frequency domain. To design the robust PID controller satisfying disturbance attenuation and robust tracking property for a reference input, first an H$\infty$ controller satisfying given performance is designed using the H$\infty$ control method. And then, the parameters(proportional, integral, and derivative gains) of the robust PID controller are determined using the model matching at frequency domain. The proposed technique is applied to a position controller design of the web. The simulation results show that the proposed robust PID controller satisfies disturbance attenuation and tracking property.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.3
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• pp.134-141
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• 2003

PID controllers with constant gains have been widely used in various control systems due to its powerful performance and easy implementation. But it is difficult to have uniformly good control performance in all operating conditions. In this paper, we propose a nonlinear variable PR controller with immune feedback mechanism. An immune feedback mechanism is based on the functioning of biological T-cells, they include both an active term, which controls response speed. and an inhibitive term, which controls stabilization effect. Therefore, the proposed nonlinear PID controller is based on immune responses of biological. immune feedback mechanism which is the cell mediated immunity and In order to choose the optimal nonlinear PID controller games, we also propose the tuning algorithm of nonlinear function parameter in immune feedback mechanism. To verify performance of the proposed algorithm, the speed control of nonlinear DC motor are performed. Front the simulation results, we have found that the proposed algorithm is more superior to the conventional constant fain PID controller.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.247-256
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• 1999

In this paper, a methodology for optimal design of PID controller using the modified genetic algorithm has been proposed to improve the transient stability at system fault in HVDC transmission system, mathematical model preparation for stability analysis, and supplementary signal control by an optimal PID controller using the modified genetic algorithm(MGA). The propriety was verified through computer simulations regarding transient stability. It means that the application of MGA-PID controller in HVDC transmission system can contribute the propriety to the improvement of the transient stability in HVDC transmission system and the design of MGA-PID controller has been proved indispensible when applied to HVDC transmission system.

• Journal of Drive and Control
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• v.14 no.4
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• pp.23-28
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• 2017

Time-delay control with internal model (TDCIM) is the controller for robot manipulators that applies the time-delay estimation and the concept of internal model control (IMC). TDCIM is robust against unknown dynamics and non-linear friction like coulomb friction and static friction. It is simple and computationally efficient. This study presents the relationship between the discrete TDCIM and the discrete PID controller. The PID controller is the most popular control law in the real application. But often the PID controller can be difficult to achieve the desired level of control performance. The result in this study provides a good candidate solution to these situations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.3
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• pp.536-544
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• 2011

In this paper, a controller for two wheeled inverted pendulum type robot is designed to have more stable balancing capability than conventional controllers. Traditional PID control structure is chosen for the two wheeled inverted pendulum type robot, and proper gains for the controller are obtained for specified user's weights using trial-and-error methods. Next a neural network is employed to generate PID controller gains for more stable control performance when the user's weight is arbitrarily selected. Through simulation studies we find that the designed controller using the neural network is superior to the conventional PID controller.