• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.146-156
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• 2003

Nonlinear dynamic system exist widely in many types of systems such as chemical processes, biomedical processes, and the main steam temperature control system of the thermal power plant. Up to the present time, PID Controllers have been used to operate these systems. However, it is very difficult to achieve an optimal PID gain with no experience, because of the interaction between loops and gain of the PID controller has to be manually tuned by trial and error. This paper suggests control approaches by immune fuzzy for the nonlinear control system inverted pendulum, through computer simulation. This paper defines relationship state variables $x,\dot{x},{\theta},\dot{\theta}$ using immune fuzzy and applied its results to stability.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.700-702
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• 2000

The new design methodology of a hybrid fuzzy controller by means of the genetic algorithms is presented. In fuzzy controller which has been widely applied and used. in order to construct the best fuzzy rules that include adjustment of fuzzy sets, a highly skilled techniques using trial and error are required. To deal with such a problem, first, a hybrid fuzzy controller(HFC) related to the optimal estimation of control parameters is proposed. The HFC combined a PID controller with a fuzzy controller concurrently produces the better output performance than any other controller from each control output in steady state and transient state. Second, a auto-tuning algorithms is presented to automatically improve the performance of hybrid fuzzy controller, utilizing the simplified reasoning method and genetic algorithms. In addition, to obtain scaling factors and PID Parameters of HFC using GA, three kinds of estimation modes such as basic, contraction, and expansion mode are effectively utilized. The HFCs are applied to the first-order second-order process with time-delay and DC motor Computer simulations are conducted at step input and the performances of systems are evaluated and also discussed from performance indices.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.525-530
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• 2003

This paper focuses on design of nonlinear power plant controller using immune based multiobjective fuzzy approach. The thermal power plant is typically regulated by the fuel flow rate, the spray flow rate, and the gas recirculation flow rate. However, Strictly maintaining the steam temperature can be difficult due to heating value variation to the fuel source, time delay changes in the main steam temperature. the change of the dynamic characteristics in the steam-turbine system. Up to the present time, PID Controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. These parameters tuned by multiobjective based on immune network algorithms could be used for the tuning of nonlinear power plant.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.128.4-128
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• 2001

Based on fuzzy logic algorithm this paper constructed fuzzy logic controller for automated vehicles. For passenger´s convenience especially comfortability controller need to reduce the frequency of input variable´s changing. So we established membership functions for comfortability as well as speed following. It made possible to control comfortability directly. To demonstration the efficiency of fuzzy logic controller, we carried out simulation with a automobile´s transfer function. First, we designed the PID controller by using Ziegler-Nichols tunning method. Second, we calculated time response for each controller, then we compared the speed patterns of fuzzy controlled system and PID controlled system. Also we compared the difference of input variable ...

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.345-348
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• 1991

This paper describes a new auto tuning method for the intelligent PID control system. This new method is hosed on the settling time of the process and has been introduced into auto-tuning PID controller using fuzzy logic. The performance of the controller is measured by computer simulation. Simulation shows good results that controller searches well the optimal values of PID parameters in any conditions and the response characteristic of the control system is improved.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.803-805
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• 1999

Robust fuzzy logic control, PID control, and sliding mode controllers are designed to control the speed of a third order linear time-invariant model of a motor. The step response performance of each controller, applied to the motor plant, is Presented. We conclude fuzzy logic control can be a useful tool for the control engineering.

• Journal of Industrial Technology
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• v.13
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• pp.43-48
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• 1993

In this paper, a new PID fuzzy controller (FC) is presented. The linguistic control rules of PID FC is separated into two parts : one is $e-{\Delta}e$ part, and the other is ${\Delta}^2e-{\Delta}e$ part. And then two FCs employing these rule base indivisually are synthesized. The control input to the process is decided by taking weighted mean of the outputs of two FCs. The proposed PID FC improve the transient response of the system and gives better performance than the conventional PI FC.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.901-904
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• 1998

In this paper, we describes PID-neural network controller for the rotary inverted pendulum. PID control is applied to many fields but has some problems in nonlinear system due to a variation of parameter. So, we should desing the controller which is adjusted PI parameters by the neural network which is learned by backpropagation algorithm. And we show that on-line control is possible through the PID-neural network controller. The angle of the pendulum is controlled and then the position of the rotating arm is also controlled to maintain with in the set point. Measurement of the pendulum angle is obtained using a potentionmeter. The objective of the experiment is to design a PID-neural network control system that positions the arm as well as maintains the ivnerted pendulum vertical. Finally, we describe the actual experiment system and confirm the experimental results.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.732-734
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• 1997

This paper describes the design and implementation of a fuzzy temperature control system to control the water temperature in plant. For evaluating the performance of a fuzzy controller, the experiment results of a fuzzy controller are compared with these of a conventional PID controller which provides an auto-tuning function. The experiment results show that the proposed controller has a good control performance and is robust to external disturbance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.342-345
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• 2003

This paper presents an adaptive PID control scheme for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model is used to estimate the error of control input, and the parameter of PID controller are adapted using the error. The parameters of TSK fuzzy model are also adapted to plant. The proposed algorithm allows designing adaptive PID controller which is adapted to the uncertainty of nonlinear plant and the change of parameters. The usefulness of the proposed algorithm is also certificated by the several simulations.