• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.504-506
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• 1999

In this paper, we propose a method which control parametric uncertainty systems using PID controller by fuzzy auto tuning. We get the error and the error change rate of plant output correspond to the initial value of parameter using the Ziegler-Nickols tuning and determine the new proportional gain$(K_p)$ and the integral time $(T_i)$ from fuzzy tuner by the error and error change rate of plant output as a membership function of fuzzy theory. The Fuzzy Auto-tuning algorithm for PID controller operate to adapt variable parameter of plant in parametric uncertainty systems. It is shown this method considerably improve the transient response at computer simulation.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.775-777
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• 1998

The Ziegler-Nichols parameter tuning has been widely known as a fairly heuristic method to good determine setting of PID controllers, for a wide range of common industrial processes. We extract process knowledge required for rule base controller through tuning experiment and simulation study, such as set point weighting and normalised gain and dead time of process. In this paper, we presents a rule base PID controller by extracted process knowledge and the modified Ziegler-Nichols tuning. Computer simulation are provided demonstrate the feasibility of this approach.

• Proceedings of the KIEE Conference
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• 1984.07a
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• pp.33-35
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• 1984

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.168-172
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• 1994

This paper shows that the GPC with exponential weighting(GPCEW) can be applied to Electric furnace system which has large time delay. Stability of GPCEW can be guarantee from monotonically non-increasing property of Riccati difference equation. We show that the performance of GPCEW versus GPC and auto-tuning PID control is better than that of GPC or atito-tuning PID.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.5
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• pp.485-493
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• 1990

The idea of expert control is to incorporate a rule based expert system in a feedback control system. In this paper, we present some heuristic rules based on relay experiment for the choice of controller structure and the setting of the controller parameters. Heuristic rules are used as an element of the feedback loop in an auto-tuning PID controller. The algorithms are coded in a form which is as pure as possible and the heuristic logic is implemented with the rules. This paper reports an implementation of an expert controller on microcomputer-based system, including an industrial programmable controller.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.1
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• pp.39-47
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• 2014

In this study, 2 degree of freedom PID controller is added to the conventional feed-forward controller for the purpose of improving its limitations such as set-point of tracking performance and disturbance suppression performance in the conventional PID controller. And the controller parameters optimization as a Real Coded Genetic Algorithm (RCGA) is used. Simulation and experiments verify the performance of the controller.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.1 s.173
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• pp.7-15
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• 2000

This paper suggests an auto-tuning I'll) control algorithm that uses the advantage of PID controller and improves the system performance. The PID gains being designed by th- conventional method are tuned through the plant parameter estimation. The Extended Kalman Filter is used for the estimation. It works as an observer and noise filter. Moreover, as the plant state and the uncertain parameter could be estimated simultaneously, the proposed algorithm is very useful in the tracking control of a system with uncertain parameter. The auto-tuning I'll) controller could maintain the system performance in the case that the plant parameters are uncertain or varying. The proposed control algorithm requires a correct estimation of the plant parameter. The controller stability and the performance is considered through the stability criteria and a servo motor model. The Kalman filter estimates the most sensitive plant parameter, which is determined by the sensitivity analysis.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.650-652
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• 2000

Although Fuzzy Logic Controller(FLC) adopted three terms as input gives better performance. FLC is in general composed of two-term control because of the difficulty in the construction of fuzzy rule base. In this paper, a three-term FLC which is similar to PID control but acts as a nonlinear controller is proposed. To reduce the complexity of the rule base design and increase efficiency, a simplified fuzzy PID control is induced from a hybrid velocity/position type PID algorithm by sharing a common rule base for both fuzzy Pi and fuzzy PD parts. It is simple in structure, easy in implementation, and fast in calculation. The phase plane technique is applied to obtain the rule base for fuzzy two-term control and them. The resultant rule base is Macvicar-Whelan type. The frequency response information is used in tuning of membership functions. Also a tuning strategy for the scaling factors is Proposed based on the relationship between PID gain and them. Simulation results show better performance and the effectiveness of the proposed method.

• 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.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.7 no.4
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• pp.42-50
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• 1993

This paper was proposed drive strategy Induction Motor using the fuzzy algorithm of auto-tuning PID control. In fuzzy control, the 1M driving system is controlled in trial and error without the mathematical modeling and using fuzzy lookup table the real time control is possible. Also, ,dividing the fuzzy rules in several zone, the stability and response of the 1M driving system is improved. The parameters of 1M are varying according to the environmental conditions, the variance of the parameters is affected with the driving characteristics of 1M. Using the fuzzy algorithm of the driving system which has the auto tuning control function for high performance, high accuracy of the driving system, a designed and proposed through the comparision with the PID control method and the driving characteristics is reviewed and analyzed.