• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.346-350
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• 1989

This paper concerned about a study on the position control of KED-1 robot manipulator using PID self-tuning controller. For two joint manipulator KED-1, KED-1 manipulator is translated into kinematics to control each joints in joint space seheme. Angle displacements of joint coordinate are transformed into reference angle velocity of each subsystem through trajectory planning. Also, time sharing technique is used to control KED-1 manipulator. A series of simulation and experiment are performed for each joint in show the valid of proposed algorithm.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.659-661
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• 1999

In this paper, we proposed a simple neural network-based parameter tuning algorithm, which could find the gradients of a certain performance index in the PID parameter spaces. In this process, we had to know the dynamics between input and output of the plant, and we used the Back Propagation Neural network to identify them. To make the parameter updating fast and smooth, we constructed the performance index as the sum of past N-squared plant errors, and applied a batch mode algorithm to update parameters. We performed several experiments with a DC Motor to show the validity of the proposed algorithm.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.333-336
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• 1992

In this paper, We propose a PID-type of self-tuning algorithm which is based on the parameter estimation and the minimization of the cost function. We use the CARIMA model for parameter estimation and determine the discrete PID controller parameters by minimizing the cost function which considers the quadratic deviations of the predicted output over the set-point as well as the control efforts. Also, The algorithm is extended by incorporating constraints of the control signal. Simulations are performed to illustrate the efficiency of the proposed method.

• Proceedings of the KAIS Fall Conference
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• 2009.05a
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• pp.458-460
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• 2009

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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.12 no.3
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• pp.7-13
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• 1998

In this paper, the control technique that ID controller are autotuned according to system dynamics, driving out sample in the changeable limits of system dynamics and learning neural network, is presented. In order to lean neural network, the backpropagation learning algorithm is used and the controller parameters obtained by rule-base are used as teacher's values. When load changes, the auto-tuning of PID controller proper to system dynamics is conformed by simulation.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.79-81
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• 1987

Expert systems have a variety of potential applications in process control. The application domain ranges from the entire plant system to a single loop system. Both, off-line and real-time problems may be realized. In this paper, expert system is employed as a part of a single control loop of PID Controller with self-tuning. The goal of expert system in the present study is to build up the necessary process knowledge required for efficient control. In order to achieve this process, the development of an expert system and a prototype model is carried out. OPS5, a rule based production system, is utilized in experiment, and common LISP is used for man-machine interface.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2120-2122
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• 2002

There are several types of control systems that use fuzzy logic controller as a essential system component. The majority of research work on fuzzy PID controller focuses on the conventional two-input PI or PD type controller. However, fuzzy PID controller design is a complex task due to the involvement of a large number of parameters in defining the fuzzy rule base. In this paper we combined conventional PI type and PD type fuzzy controller and set the initial parameters of this controller from the conventional PID controller gains obtained by Ziegler-Nichols tuning or other coarse tuning methods. After that, by replacing some of these parameters with sing1e neurons and making them to be adjusted by back-propagation learning algorithm we designed a neuro-fuzzy controller which showed good performance characteristics in both computer simulation and actual application.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.702-704
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• 1997

In this paper, we applied self-tuning controller with I-PD type to process with time delay's. Process parameters are estimated by the recursive least squares algorithm, and optimal gains are obtained. This paper shows self-tuning controller with I-PD type performs better than that of general PID type for the nonlinear system with sudden change of set-points.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.5
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• pp.505-508
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• 2006

This paper presents a new analytical approach for designing multiloop PI controllers for disturbance rejection in multivariable processes with time delay. The proposed method is based on IMC-PID design approach. To overcome a sluggish load response by dominant pole in the process, the IMC filter is modified to compensate the dominant pole effect. Based on the modified IMC filter, an analytical tuning rule for multiloop PI controller is driven by extending the generalized IMC-PID method for single input/single output (SISO) systems [1] to multi input/multi output (MIMO) systems. Simulation results show that the proposed method gives a satisfactory load performance as well as servo performance in the multiloop system.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.496-498
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• 1998

This paper presents a mathod of reducing hunting size or steady state error occurred in the output signals via regulating the PID controllers gains. The PID controllers are widely used in industrial processes. Such processes have several inherent features like continuous operation, fixed set value, and difficulty in applyirty test signals. Thus, this paper suggests fuzzy rules of reducing hunting magnitude or steady state error using output signals only. Such an intelligent tuning technique utilizes both the experts, experience and control engineers' theortical background. For two kinds of systems such as temperature or DC motors speed control, we showed the validity of proposed method in this paper.