• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.9 no.4
• /
• pp.309-314
• /
• 2009

This paper presents implementation of the adaptive neuro-fuzzy control method. Control performance of the adaptive neuro-fuzzy control method for a popular inverted pendulum system is evaluated. The inverted pendulum system is designed and built as an education kit for educational purpose for engineering students. The educational kit is specially used for intelligent control education. Control purpose is to satisfy balancing angle and desired trajectory tracking performance. The adaptive neuro-fuzzy controller has the Takagi-Sugeno(T-S) fuzzy structure. Back-propagation algorithm is used for updating weights in the fuzzy control. Control performances of the inverted pendulum system by PID control method and the adaptive neuro-fuzzy control method are compared. Control hardware of a DSP 2812 board is used to achieve the real-time control performance. Experimental studies are conducted to show successful control performances of the inverted pendulum system by the adaptive neuro-fuzzy control method.

• Journal of the Korean Society for Precision Engineering
• /
• v.12 no.12
• /
• pp.139-148
• /
• 1995

In this paper an electro-hydraulic servo system is designed by using a fuzzy control algorithm. In order to drive an optimal fuzzy control system, a simulation program for the control system has been developed. By this program the fuzzifier and defuzzifier, a fuzzy inference method, a fuzzy relational matrix, and a fuzzy inference method are investigated. As a result, Larsen inference method, 9*9 fuzzy relational matrix, and center of area defuzzifier are turned out the best as parameters. Finally this method is compared with the conventional PID algotithm, and showed that the fuzzy control performs better than PID algorithm. The fuzzy control performs very well adap- tation against uncertain disturbances.

• Journal of Mechanical Science and Technology
• /
• v.15 no.5
• /
• pp.580-591
• /
• 2001

This paper proposes a fuzzy-sliding mode control which is designed by a self tuning fuzzy inference method based on a genetic algorithm. Using the method, the number of inference rules and the shape of the membership functions of the proposed fuzzy-sliding mode control are optimized without the aid of an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. It is further guaranteed that the selected solution becomes the global optimal solution by optimizing Akaikes information criterion expressing the quality of the inference rules. In order to evaluate the learning performance of the proposed fuzzy-sliding mode control based on a genetic algorithm, a trajectory tracking simulation of the polishing robot is carried out. Simulation results show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the trajectory control result is similar to the result of the fuzzy-sliding mode control which is selected through trial error by an expert. Therefore, a designer who does not have expert knowledge of robot systems can design the fuzzy-sliding mode controller using the proposed self tuning fuzzy inference method based on the genetic algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.11a
• /
• pp.524-528
• /
• 1996

Quality information is known to have the characteristic of continuous distribution in many manufacturing processes. It is difficult to describe the process condition by classifying the distribution into discrete ranges which is based on the set concept. Fuzzy control chart has been developed for the control of linguistic data but it still utilizes the dichotomous notion of classical set theory. In this paper, the fuzzy sampling method is studied in order to manage the ambiguous data properly and incorporated for generating fuzzy control chart. The method is based on the fuzzy set concept and considered to be appropriate for the realization of a complete fuzzy control chart. The fuzzy control chart was compared with the conventional generalized p-chart in the sensitivity for quality distribution and robustiness against the noise. The fuzzy control chart with the fuzzy sampling method showed better characteristics.

• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.1127-1128
• /
• 2008

In this paper, we propose a control method to improve control performance for a Quad-rotor Unmanned Aerial Vehicle's stabilization. The proposed method is the Fuzzy+I control that contains a fuzzy controller which processes signals from the error and the change of error, and generates the control signal by summing up fuzzy output signal and integral signal. We simulated and experimented on the fuzzy+I control method by implementing Quad-rotor UAV that is able to hovering, for the purpose of verifying the effectiveness of the proposed fuzzy+I control method in comparison with general PID control, and we found out that fuzzy+I controller improved control performance of the system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1993.06a
• /
• pp.949-952
• /
• 1993

As well-known, fuzzy control has been recognized to be of great usefulness in many engineering fields. However, the present design methods of fuzzy control systems depend on trial and error the thing that limits its usefulness. Therefore, an effective and convenient support tools for design and evaluation are greatly needed as well as the establishment of the design methods and guidling. From these backgrounds, we have developed a fuzzy control simulator[1, 2] which has various fuzzy control methods such as "direct method", "indirect method" and "fuzzy-PID method". This paper deals especially with the "direct method" function of the simulator. The simulator was developed for personal computers and programed in C language.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.5 no.7
• /
• pp.1374-1383
• /
• 2001

In the fuzzy control method behaves more robustness than conventional control method, we propose a intelligent control method that membership functions and scaling factor of the fuzzy logic controller are optimized by genetic algorithm under off-line, and then fuzzy logic controller is constructed by the optimization parameters under on-line. In order to verify the usefulness of the proposed control method, we are applied to one link manipulator, and confirmed that the proposed control method is reduced the fuzzy rule base and is the better performance than the conventional fuzzy control method.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.67 no.12
• /
• pp.1699-1704
• /
• 2018

In this paper, proposed a method to improve of control characteristics for power facility using the adaptive sizing of fuzzy inference method. In the use of the controller based the fuzzy logic, a basic mamdani fuzzy controller is applied. However, when the maximum value and the minimum value have to taken, the fuzzy controller can not take a normal value because of formalized grouping form. In this paper, we combine the conventional methods with single valued sets to compensate for the disadvantage caused by the mamdani method control. Simulation results show that the proposed method has better overshoot and steady state arrival time than the conventional control method.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.9 no.3
• /
• pp.97-103
• /
• 1995

In this paper, for fuzzy control of linear servo system using the moving coil type linear DC motor, we propose a new fuzzy control method using parameter tuning for membership functions. A proposed fuzzy control method tunes parameters of membership function to have an appropriate control input signal for system when error exceeds predefined value and makes an inference using conventional fuzzy control rules when error reduces to a predefined value. To verify usefulness of a proposed fuzzy control method, making simulation and experiment, we compare with characteristics for conventional fuzzy control method.

• Transactions on Control, Automation and Systems Engineering
• /
• v.3 no.1
• /
• pp.58-65
• /
• 2001

This paper proposes a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a robot. Using this method, the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. And, it is guaranteed that he selected solution become the global optimal solution by optimizing the Akaikes information criterion expressing the quality of the inference rules. The trajectory tracking simulation and experiment of the polishing robot show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding mode controller provides reliable tracking performance during the polishing process.