• Journal of the Korean Society for Precision Engineering
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• v.20 no.11
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• pp.91-99
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• 2003

When the driver suddenly depresses the brake pedal under critical conditions, the desired trajectory of the vehicle can be changed. In this study, the vehicle dynamics and fuzzy logic controller are used to control the vehicle trajectory. The dynamic vehicle model consists of the engine, the rotational wheel, chassis, tires and brakes. The engine model is derived from the engine experimental data. The engine torque makes the wheel rotate and generates the angular velocity and acceleration of the wheel. The dynamic equation of the vehicle model is derived from the top-view vehicle model using Newton's second law. The Pacejka tire model formulated from the experimental data is used. The fuzzy logic controller is developed to compensate for the trajectory error of the vehicle. This fuzzy logic controller individually acts on the front right, front left, rear right and rear left brakes and regulates each brake torque. The fuzzy logic controlling each brake works to compensate for the trajectory error on the split - $\mu$ road conditions follows the desired trajectory.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.161.4-161
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• 2001

This paper presents adaptive control of robot manipulator using neuro-fuzzy controller Fuzzy logic is control incorrect system without correct mathematical modeling. And, neural network has learning ability, error interpolation ability of information distributed data processing, robustness for distortion and adaptive ability. To reduce the number of fuzzy rules of the FLS(fuzzy logic system), we consider the properties of robot dynamic. In fuzzy logic, speciality and optimization of rule-base creation using learning ability of neural network. This paper presents control of robot manipulator using neuro-fuzzy controller. In proposed controller, fuzzy input is trajectory following error and trajectory following error differential ...

• Proceedings of the KSR Conference
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• 2000.11a
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• pp.652-658
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• 2000

TCMS(Train Control ＆ Management System) control monitor and test the main on-board equipments including propulsion/brake unit by the serial transmission line. TCMS reduces interface circuits and number of train lines by the software logic and utilizing serial communication method. This paper describes the method of powering and braking control by TCMS software logic, and the software logic is verified by running test at Seoul Subway Line＃ 6. By running test result, we can see TCMS successfully control Powering/Braking of train

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.1
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• pp.59-66
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• 2000

As the highly complex and precise control functions are required in modern industrial process control system, the complex function models and developed in each hardware and software of PLC. The SFC graphical language is very powerful for describing the sequential logic control algorithm, on the other hand it graphical language is very powerful for describing the sequential logic control algorithm, on the other hand it has problems in describing the interlock logic control algorithm, such as error management algorithm. In this paper, we propose the efficient error management method using the action qualifiers to design the error management algorithm in industrial process control system described scheme, we realize the error management logic in process control system of film coating machine.From the experiment results, we confirm that the proposed scheme is very useful in aspects to realize easily th error management logic and to reduce the memory capacity for user's program.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.3
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• pp.295-300
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• 2018

Conventionally, a PI control algorithm has been widely used as a speed control algorithm for BLDC motor. The PI control algorithm has a disadvantage in that is slow to reach the steady state due to the slow speed and torque response with various speed changes. Therefore, in this paper, PWM fuzzy logic control algorithm which can reach the steady state quickly by improving the response speed although there is a little overshoot is proposed. PWM reduces response speed and fuzzy logic control algorithm minimizes overshoot. The proposed PWM fuzzy logic control algorithm consists of DC chopper, PWM duty cycle regulator, and fuzzy logic controller. The performance and validity of the proposed algorithm is verified by simulation with Simulink of Matlab 2018a.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.171-176
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• 2001

This paper describes the methodology of an IEC 1131-3-based control logic generator for the control system design and converting algorithm between programmable languages. The proposed control logic generator is generated based on the software model and common element with data type, variables, POUs(program organization unit) and execution control unit commonly used within programmable languages of IEC 1131-3 Standard. The generation method of object file was proposed on five programmable language based on IECI 131-3. The generation method of object file is represented as following; 1) the generation method using conversion algorithm from LD to IL with FBD(function block diagram), 2) the generation method using C code generation algorithm from SFC using the SFC execution sequence with FBD and ST(structured text). The proposed control logic generator was implemented by Visual C++ and MFC on MS-windows NT 4.0

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.452-457
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• 1999

The recent control system has been changed into the type of PLC(Programmable Logic Controller) control. Up to now, systematic approach of PLC programming or control logic design is not suggested. In this study, the design process of the control logic is systematized and concrete process in each step is suggested. This systematized approach lead developer to be convenient to implement control system. When some error is occurred in the system, this approach enable the developer to analyze the reason of error rapidly and the system is amended according to systematic information of the analysis. The example of control system implementation following this approach is introduced.

• Journal of the Korean Institute of Intelligent Systems
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• v.3 no.4
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• pp.48-59
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• 1993

Intellignet control of the dyeing machine is a central part to improve the productivity of autonomous dyeing systems. Recently, many number of control methods are introuduced. One of them is fuzzy logic algorithm. Fuzzy logic based controller has many desirable advantages, which are simple to implement on the real time and need not the information of dynamic characteristics of the systems. In this paper we propose a new dyeing machine control simulator using fuzzy logic algorithm as an approach to develop the intellingent auto-dyeing control system. This developing approach of the fuzzy control simulator is based on linguistic control stratege of experts.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.47-51
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• 2001

According as the controlled plants become more complex and large-scaled, the development of more intelligent control schemes is required in the control field. A fuzzy logic control (FLC) is one of proper schemes for this tendency. Recently, fuzzy control has been applied successfully to many industrial applications due to a number of advantages. But it still has some disadvantages. The conventional FLC has many tuning parameters: membership functions, scaling factors, and so forth. In order to improve this problem, a single-input fuzzy logic control (SFIC) which greatly simplifies the design process of the conventional FLC was proposed. Many research has also been proposed to develop the stability analysis of the FLC. In this paper we analyze the absolute stability of the SFLC. We first expand a nonlinear controlled plant into a Taylor series about a nominal operating point. And a fuzzy control system is transformed into a Lure system with nonlinearities. We also prove that the closed-loop system with the SFLC satisfies the sector condition globally.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.215-219
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• 1998

This paper presents a practical application of fuzzy logic control to temperature control of glass melting furnace. Due to the characteristics of glass melting furnace, a hybrid algorithm of conventional PI controller and fuzzy logic controller is proposed and discussed. Practical implementation results of the production furnace showed the effectiveness of the proposed control algorithm.