• The Transactions of the Korean Institute of Electrical Engineers P
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• v.53 no.3
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• pp.134-141
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• 2004

Recently, neural network techniques are widely used in adaptive and learning control schemes for production systems. However, in general it takes up a lot of time to learn in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult for the PID gains suitably, lots of researches have been reported with respect of turning schemes of PID gains. A neural network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed neural network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accidents.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.89-91
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• 2004

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult to the PID gains suitably lots of researches have been reported with respect to turning schemes of PID gains. A Neural Network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident. This paper goal is convergence speed progress about applied gain of PID controller using the neural networks.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.2
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• pp.197-203
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• 1997

In this paper, we present a GP-PID(Generalized Predictive PID) controller which has the same structure as a generalized predictive control with steady-state weighting. The proposed controller can perform better than the conventional PID controller because it includes intrinsic delay-time compensator. The PID tuning parameters and delay-time compensator are calculated by equating the two degree of freedom PID to a linear form of GPC. The proposed controller is combined with a supervisor for safe start and self-tuning. GP-PID controller has been tested for various numerical models and an experimental stirred tank heater. As a result, it was observed that the proposed controller shows a satisfactory performance for variable delay as well as stochastic disturbance.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.717-719
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• 2003

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult to the PID gains suitably lots of researches have been reported with respect to turning schemes of PID gains. A Neural Network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.9
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• pp.813-821
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• 2000

In this paper, we developed modeling of tension and speed dynamics for a two-drum winder in a three span continuous web transport system which had not been previously. Dynamic modeling of the time-varying nonlinear system was derived by considering the effect of the radii and mass moment of inertia in the unwinder and the two-drum winder through winding up the web. After linearizing it, we designed with a variable-gain a PID controller for tension control and a PI controller for speed. Simulation is carried out with the variation of radii and moment of inertia at high speed for the proposed tension control system with the two-drum winder and the variavle-gain a PID controller. Results show good performance of tension control during the speed change speed at a start-up and stop.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.756-762
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• 2006

Shape Memory Alloy(SMA) actuators, which have the ability to return to a predetermined shape when heated, have many potential applications such as aeronautics, surgical tools, robotics and so on. Although the conventional PID controller can be used with slow response systems, there has been limited success in precise motion control of SMA actuators, since the systems are disturbed by unknown factors beside their inherent nonlinear hysteresis and changes in the surrounding environment of the systems. This paper presents a new development of a SMA position control system by using a self-tuning fuzzy PID controller. This control algorithm is used by tuning the parameters of the PID controller thereby integrating fuzzy inference and producing a fuzzy adaptive PID controller, which can then be used to improve the control performance of nonlinear systems. The experimental results of position control of SMA actuators using conventional and self-tuning fuzzy PID controllers are both included in this paper.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2268-2277
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• 2017

As constantly increasing wind power penetrates power grid, wind power plants (WPPs) are exerting a direct influence on the traditional power system. Most of WPPs are using variable speed constant frequency (VSCF) wind turbines equipped with doubly fed induction generators (DFIGs) due to their high efficiency over other wind turbine generators (WTGs). Therefore, the analysis of DFIG has attracted considerable attention. Precisely measuring optimum reference speed is basis of utilized maximum wind power in electric power generation. If the measurement of wind speed can be easily taken, the reference of rotation speed can be easily calculated by known system's parameters. However, considering the varying wind speed at different locations of blade, the turbulence and tower shadow also increase the difficulty of its measurement. The aim of this study is to design fuzzy controllers to replace the wind speedometer to track the optimum generator speed based on the errors of generator output power and rotation speed in varying wind speed. Besides, this paper proposes the fuzzy adaptive PID control to replace traditional PID control under rated wind speed in variable-pitch wind turbine, which can detect and analyze important aspects, such as unforeseeable conditions, parameters delay and interference in the control process, and conducts online optimal adjustment of PID parameters to fulfill the requirement of variable pitch control system.

• Journal of Advanced Marine Engineering and Technology
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• v.22 no.3
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• pp.320-327
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• 1998

The purpose of this study is to design the intelligent speed control system for marine diesel engine by combining the Model Matching Method and the Nominal Model Tracking Method. Recently for the speed control of a diesel engine some methods using the advanced control techniques such as LQ control Fuzzy control or H$\infty$ control etc. have been reported. However most of speed controllers of a marine diesel engine developed are still using the PID control algorithm But the performance of a marine diesel engine depends highly on the parameter setting of the PID controllers. The authors proposed already a new method to tune efficiently the PID parameters by the Model Mathcing Method typically taking a marine diesel engine as a non-oscillatory second-order system. It was confirmed that the previously proposed method is superior to Ziegler & Nichols's method through simulations under the assumption that the parameters of a diesel engine are exactly known. But actually it is very difficult to find out the exact model of the diesel engine. Therefore when the model and the actual diesel engine are unmatched as an alternative to enhance the speed control characteristics this paper proposes a Model Refernce Adaptive Speed Control system of a diesel engine in which PID control system for the model of a diesel engine is adopted as the nominal model and a Fuzzy controller is adopted as the adaptive controller, And in the nominal model parameters of a diesel engine are adjusted using the Model Matching Method. it is confirmed that the proposed method gives better performance than the case of using only Model Matching Method through the analysis of the characteristics of indicial responses.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.289-292
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• 2005

PID controllers, which have been widely used in industry, have a simple structure and robustness to modeling error. But it is difficult to have uniformly good control performance in system parameters variation or different velocity command. In this paper, we propose an adaptive PID controller based on a gradient descent learning. This algorithm has a simple structure like conventional PID controller and robustness to system parameters variation. To verify performances of the proposed adaptive PID controller, the speed control of nonlinear DC motor is performed. The simulation results show that the proposed control systems are effective in tracking a command velocity under system parameters variation.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.186-189
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• 2005

In a clinical setting, developing a reliable method for the automated drug infusion system would improve a drug therapy under the unexpected and acute changes of hemodynamics. The conventional proportional-integral-derivative (PID) controller might not be able to achieve maximum performance because of the unexpected change of the intra- and inter-patient variability. The fuzzy PID control and the conventional PID control were tested under the unexpected response of mean arterial blood pressure (MAP) to a vasopressor agent during acute hypotension. Compared with the conventional PID control, the fuzzy PID control performed the robust MAP regulation regardless of the unexpected MAP response (average absolute value of the error between target value and actual MAP: 0.98 vs. 2.93 mmHg in twice response of the expected MAP and 2.59 vs. 9.75 mmHg in three-times response of the expected MAP). The result was due to the adaptive change of the proportional gain in PID parameters.