• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.2
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• pp.55-61
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• 2000

In this paper, we proposed a new PID tuning algorithm by the fuzzy set theory to improve the performance of the PID controller. The new tuning algorithm for the PID controller has the initial value of parameter Kc, $\tau$I, $\tau$D by the Ziegler-Nichols formula using the ultimate gain and ultimate period from a relay tuning experiment. We get error and error change of plant output correspond to the initial value and new proportion gain(Kc) and integral time($\tau$I) from fuzzy tunner. This fuzzy tuning algorithm for PID controller considerably reduced overshoot and rise time compare to any other PID controller tuning algorithms. In real parametric uncertainty systems, the PID controller with Fuzzy auto-tuning give appreciable improvement in the performance. The significant properties of this algorithm is shown by simulation In this paper, we proposed a new PID algorithm by the fuzzy set theory to improve the performance of the PID controller.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.184-187
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• 1996

In this paper, an algorithm for tuning gains of a PID controller is proposed. The proposed algorithm is composed of two stages. The first is a stage for Lyapunov function-based initial stabilization of an overall system and rough tuning gains of the PID controller. The other is that for fine tuning gains of the PID controller. All tunings are performed by using the well-known fuzzy logic-based tuner. The computer simulations are performed to show the validity of the proposed algorithm and results are presented.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.272-276
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• 2004

The conventional PID (Proportional-Integral-Derivative) control technique is widely used for the process control in many industries since it is simple in structure and provides the good response. Nowadays, this control technique has been developed on the Programmable Logic Controller (PLC) to use for the process control loop. However, using this technique is difficult when tuning the PID parameters ($K_p$, $T_i$ and $T_d$) to achieve the best response. Moreover, trial-and-error procedure along with the operator experiences are required to obtain the best results when tuning the PID controller parameters. This paper proposes the self-tuning PID controller based on PLC for the process control in the industries. The proposed self-tuning PID controller uses the PLC-based PID structures to control the process production. The proposed PID tuning utilizes the PLC to synthesize and analyze controller parameter as well as to tune for appropriate parameters using Dahlin method and extrapolation. Experimental results using a self-tuning PID controller to control temperature of the oven show that the controller developed is capable of controlling the process very effectively and provides a good response.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.291-294
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• 1996

By far the PID controller is most widely sed in the process industries. However, current tuning methods yield PID parameters only for a restricted class of process models. There is no general methodology of PID controller tuning for arbitrary process models. In this paper, we generalize the IMC-PID approach and obtain the PID parameters for general models by approximating the ideal controller with a Maclaurin series. Further, the PID controller tuned by the proposed PID tuning method gave more closer closed-loop response to the desired response than those tuned by other tuning methods.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.398-403
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• 1993

A rule based fuzzy expert system to self-tune PID controllers is proposed in this paper. The proposed expert system contains two rule bases, where one is responsible for "Long term tuning" and the other for "Incremental tuning". The rule for "Long term tuning" are extracted from the Wills'map and the knowledge about the implicit relations between PID gains and important long term features of the output response such as overshoot, damping and rise time, etc., while 'Incremental tuning" rules are obtained from the relations between PID gains and short term features, error and change in error. In the PID control environment, the proposed expert system operates in two phases sequentially. In the first phase, the long term tuning is performed until long term features meet their desired values approximately. Then the incremental tuning tarts with PID gains provided by the long term tuning procedure. It is noticeable that the final PID gains obtained in the incremental tuning phase are only the temporal ones. Simulation results show that the proposed rule base for "Long term tuning" provides superior control performance to that of Litt and that further improvement of control performance is obtained by the "Incremental tuning'.ance is obtained by the "Incremental tuning'.ing'.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.10 no.1
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• pp.1-6
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• 2014

PID(Proportional, Integral, Derivative) controller is one of the most used process controllers in nuclear power plants. The optimized parameter setting of process controller contributes to the stable operation and efficiency in the operating nuclear power plants. PID parameter setting is tuned when new process control system is established or process control system is changed. It is a burdensome work for I&C(Instrument and Control) engineers to tune the PID controller because it requires a lot of experience and knowledge. When the plant is in operation, inadequate PID parameter setting can be the cause of the unstable process of the plant. Therefore the results of PID parameter setting should be compared, simulated, verified and finally optimized. The practical PID tuning methods used in process controller are tuning operation calculation(Ziegler-Nicholes, Minimum TIAE, Lambda, IMC), exclusive tuning program based on computer and Matlab application. This paper introduces the various tuning methods and suggests an optimized PID tuning process in the operating nuclear power plants.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.5
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• pp.139-147
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• 1996

PID control has been widely used for real control systems. Particularly, there are many researches on control schemes of tuning PID gains. However, to the best of our knowledge, there is no result for discrete-time systems with unknown time-delay and unknown system parameters. On the other hand, Generalized predictive control has been reported as a useful self-tuning control technique for systems with unknown time-delay. So, in this study, based on minimization of a GPC criterion, we present a self-tuning PID control algorithm for unknown papameters and unknown time-delay system. A numerical simulation was presented to illustrate the effectiveness of this method.

• Journal of Satellite, Information and Communications
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• v.11 no.4
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• pp.39-43
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• 2016

In this paper, we propose PID gain auto tuning method in steering type mobile robot. PID controller gain select method are various methods. Ziegler-Nichols step tuning method is one method tuning in PID controller. Use step tuning method find a the first gain and did experiment in steering mobile robot. and Make a new the second gains from the first gains. After appling the second gain in PID controller, Where perform observe for convergence time and stabilization error. Experiments result the second gain are useful in real steering mobile robot system.

• 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.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.10
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• pp.1031-1036
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• 2015

Proportional-integral-derivative (PID) controllers are widely used in industrial sites. Most tuning methods for PID controllers use an empirical and experimental approach; thus, the experience and intuition of a designer greatly affect the tuning of the controller. The representative methods include the closed-loop tuning method of Ziegler-Nichols (Z-N), the C-C tuning method, and the Internal Model Control tuning method. There has been considerable research on the tuning of PID controllers for single-input single-output systems but very little for multi-input multi-output systems. It is more difficult to design PID controllers for multi-input multi-output systems than for single-input single-output systems because there are interactive control loops that affect each other. This paper presents a tuning method for the PID controller for a two-input two-output system. The proposed method uses a real-coded genetic algorithm (RCGA) as an optimization tool, which optimizes the PID controller parameters for minimizing the given objective function. Three types of objective functions are selected for the RCGA, and each PID controller parameter is determined accordingly. The performance of the proposed method is compared with that of the Z-N method, and the validity of the proposed method is examined.