• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.180-180
• /
• 2000

The shape of cold strip for the stainless process has been become issue in quality recently, and hence POSCO (Pohang Iron & Steel Co., Ltd) developed an automatic control system for strip shape in the sendzimir mill. The strip shape is measured by an outward measuring roll and is controlled by As_U roll and first intermediate roll. As_U roll consists of 8 saddles, which are controlled vertically. The fist intermediate rolls, which are controlled horizontally, consist of two pairs of rolls up and down. A developed shape control system is applied to real plant by using fuzzy logic and neural network method to control actuators; As_U roll and first intermediate roll. This system composes mainly of three parts as a real-time system, input to output conditioner board, and man-machine interface. The actual shape is recognized by neural network and converted into symmetric shape. The fuzzy controller, based on the shape from neural network and sensor, controls positions of the As_U roll and first intermediate roll. This paper verifies the shape controller performance. The experiments are made on line for the sendzimir mill. The shape control performance shows very efficient for the target tracking, shape symmetry, and fluctuation of shape.

• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.2045-2047
• /
• 2001

In this paper, we proposed the representation method of specification and the control rules fork FMS by TPL. To increase productivity and resource utilization, it is desirable to have more active jobs in the system, but the more active jobs there are, the more easily deadlock occur. Therefore, it is very important that the real-time deadlock control to disable deadlock. In this paper, we performed the system modeling by the CROPN(Colored Resource -Oriented Petri Net) and the necessary -sufficient condition check to disable deadlock. We proposed the supervisory control system that can be the real-time monitoring for deadlock free through a necessary-sufficient condition by CROPN modelling, and designed the logic controller of the extended process using TPL.

• Structural Engineering and Mechanics
• /
• v.69 no.3
• /
• pp.307-315
• /
• 2019

This paper proposes a chattering-free sliding mode control (CFSMC) method for seismically excited structures. The method is based on a fuzzy logic (FL) model applied to smooth the control force and eliminate chattering, where the switching part of the control law is replaced by an FL output. The CFSMC is robust and keeps the advantages of the conventional sliding mode control (SMC), whilst removing the chattering and avoiding the time-consuming process of generating fuzzy rule basis. The proposed method is tested on an 8-story shear frame equipped with an active tendon system. Results indicate that the new method not only can effectively enhance the seismic performance of the structural system compared to the SMC, but also ensure system stability and high accuracy with less computational cost. The CFSMC also requires less amount of energy from the active tendon system to produce the desired structural dynamic response.

• Journal of the Korean Society for Precision Engineering
• /
• v.19 no.12
• /
• pp.33-37
• /
• 2002

A short shot is a molded part that is incomplete because insufficient material was injected into the mold. Remedial actions to control the process conditions can be taken by the injection molding experts based on their knowledge and experience. However, it is very difficult for the non-experts to avoid short shot by finding the proper process conditions such as mold temperature, melt temperature and filling time. In this paper, an intelligent generator of the optimal process conditions based upon fuzzy logic algorithm is proposed so that trial and error can be minimized and the non-experts as well as the experts can also find the optimal process conditions.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2001.04a
• /
• pp.402-405
• /
• 2001

A short shot is a molded part that is incomplete because insufficient material was injected into the mold. Remedial actions to control the process conditions can be taken by the injection molding experts based on their knowledge and experience. However, it is very difficult for the non-experts to avoid short shot by finding the proper process conditions such as mold temperature, melt temperature and filling time. In this paper, an intelligent generator of the optimal process conditions based upon fuzzy logic algorithm is proposed so that trial and error can be minimized and the non-experts as well as the experts can also find the optimal process conditions.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.704-707
• /
• 1997

In this paper, we present a fuzzy logic based tuner for continuous on-line tuning of PID controllers. The essential idea of the scheme is to parameterize a Ziegler-Nichols-like tuning formula by a singler parameter .alpha., then to use an on line fuzzy logic to self-tune the parameter. The adaptive scaling makes the controller robust against large variations in parametric and dynamics uncertainties in the plant model. New self-tuning controller has the ability to decide when to use PI or PID control by extracting process dynamics from relay experiments. These scheme lead to improved performance of the transient and steady state behavior of the closed loop system, including processes with nonminimum phase processes.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10a
• /
• pp.880-886
• /
• 1992

There has been continuous effort to achieve total automation of the power plants. But due to complexity, nonlinearities and time-varying properties of the system, no success has been reported if conventional and/or modern control theories are applied. In spite of this, the start-up and shutdown operations are successfully performed by skilled human operators who are able to utilize a great wealth of knowledge and past experiences. In this paper, in order to automate the shutdown operation of power plants, it is proposed that the operation be performed by a more efficient method than the current used, through dividing the total process into several subprocesses, introducing checkpoints, and using fuzzy logic. For this, fuzzy logic controllers and fuzzy decision-makers are designed and the validity is shown by simulation via a set of piecewise continuous shutdown models.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.6
• /
• pp.727-737
• /
• 2014

This paper presents the design of Fuzzy PI controller that is used at BESS(Battery Energy Storage System) charging and discharging process for islanded operation in microgrid. Most of the PI controllers have fixed PI gains, but real-time updated gains are applied to PI controller using Fuzzy logic in this paper. The performances of suggested Fuzzy PI controller are simulated by PSCAD/EMTDC. As a result, output characteristics of ESS applied real-time updated gains to PI controller are faster than those of using fixed gains.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.972-974
• /
• 1993

This paper presents a new method to automatically design fuzzy logic controller(FLC). The main problems of designing FLC are how to optimally and automatically select the control rules and the parameters of membership function (MF). Cell state space algorithms (CSS), differential competitive learning (DCL) and multialyer neural network are combined in this paper to solve the problems. When the dynamical model of a control process is known. CSS can be used to generate a group of optimal input output pairs(X, Y) used by a controller. The(X, Y) then can be used to determine the FLC rules by DCL and to determine the optimal parameters of MF by DCL and to determine the optimal parameters of MF by multilayer neural network trained by BP algorithm.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.9 no.6
• /
• pp.591-597
• /
• 1999

When fuzzy logic controllers which are designed based on plant models and intuitive base are applied to real plants, the control systems may not give satisfactory control results due to the modeling error and the lack of knowledge on the plants. In that case. the controller must be retuned by adjusting the control parameters; this retuning process may require a large number of trial-and-error evaluations and thus much time and cost. In order to resolve these problems, we propose a systematic and efficient procedure for designing a fuzzy logic controller using response surface methodology. First wc select the initial optimal conditions of control parameters using a genetic algorithm, in which a nominal plant model with intrinsic modeling errors is used. And then we determine the tinal optimal conditions of the control parameters using response surface methodology. Computer simulations are performed to verify the capability of the proposed method.