• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.7-10
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• 1998

Because of simple control algorithm and easy implementation, the conventional PI controller has been widely used in industrial application. But, it is very difficult to find the optimal PI control gain. Therefore, in improperly tuned PI controller or parameter variation, to obtain optimal performance, the novel PI controller, which consist of conventional PI controller and 4-rule based fuzzy logic, are presented in this paper. The novel PI controller which exhibits a stabilizing effects on the closed-loop system, has good robustness regarding the improperly tuned PI controller or parameter variation. The simulations are performed to verify the capability of proposed control method on induction motor.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.869-873
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• 1992

A fuzzy controller is designed for compensating the cross-coupling effect of induced roll due to the dynamic characteristics of three fin torpedo. Since the utilization of fuzzy-coprocessor has many interfacing problems with typical microprocessors of the guidance and control unit, the simplified fuzzy inference method based on nonfuzzy-processor is proposed to implement fuzzy controllers of three fin torpedo. This method provides a flexible rule-base design to guarantee the robust control. The good potential of the proposed design is shown through real-time simulations using both a mathematical model on AD-100 computer and an implemented controller on Intel 80C186/80C 187 microprocessors employing 12bit A/D converter.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.243-248
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• 1996

In this paper, a general neural-network-based connectionist model, called Fuzzy Neural Network(FNN), is proposed for the realization of a fuzzy logic control system. The proposed FNN is a feedforward multi-layered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. Such FNN can be constructed from training examples by learning rule, and the connectionist structure can be trained to develop fuzzy logic rules and find optimal input/output membership functions. Computer simulation examples will be presented to illustrate the performance and applicability of the proposed FNN, and their associated learning algorithms.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.3
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• pp.861-871
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• 1996

In this study, a fuzzy control algorithm was developed for the randomly irregular shape of cold-rolled strip. Currently developed fuzzy control algorithm consists of two parts: the first part calculates the changes of work and intermediate roll bender forces based on the symmetric part of the irregular strip shape, and the second part calculates the weighting factors based on the asymmetric part and modifies the pre-determined roll bender forces according to the weighting factors. As a result of this, bender froces applied at the both sides of the cold-rolled strip were different. In order to simulate the continuous shape control. fuzzy controller developed was linked with emulator which was developed based on neural network. The fuzzy controller and emulator developed simulated the cold rolling process until irregular shape converged to a tolerable range in producing uniform cross-sectional strip shape. The results obtained from the simulation were reasonable for various irregular strip shapes.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.2
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• pp.180-185
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• 2003

This paper proposes an intelligent navigation algorithm for multiple mobile robots under unknown dynamic environment. The proposed algorithm consists of three basic parts as follows. The first part based on the fuzzy rule generates the turning angle and moving distance of the robot for goal approach without obstacles. In the second part, using both fuzzy and neural network, the angle and distance of the robot to avoid collision with dynamic and static obstacles are obtained. The final adjustment of the weighting factor based on fuzzy rule for moving and avoiding distance of the robots is provided in the third stage. The experiments which demonstrate the performance of the proposed intelligent controller is described.

• Journal of Advanced Navigation Technology
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• v.9 no.1
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• pp.34-40
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• 2005

In this paper we propose a turbojet engine controller of unmanned aircraft based on the Fuzzy-PI algorithm. To prevent any surge or a flame out event during the engine acceleration or deceleration, the PI-type fuzzy controller effectively controls the fuel flow input of the control system. The fuzzy inference rule made by the logarithm function of acceleration error improves the tracking error. Computer simulations applied to the linear model of a turbojet engine show that the proposed method has good tracking performance for the reference acceleration and deceleration commands.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.505-508
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• 1997

In this paper, an auto-tuning method for fuzzy controller based on the neural network is presented. The backpropagated error of neural emulator offers the path which reforms the fuzzy controller's membership functions and fuzzy rule, and used for speed control of induction motor. For the torque control method, an indirect vector control scheme with slip calculation is used because of its stable characteristics regardless of speed. Motor input current is regulated by a current controlled voltage source PWM inverter using space voltage vector technique. Also, the scheme of current control fuzzy controller is synchronous reference frame with decoupling term. DSP(TMS320C31) is used to achieve the high speed calculation of the space voltage vector PWM and to build the self-learning fuzz. control algorithm. An IPM is used to simplify hardware design.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.93.2-93
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• 2001

This paper presents the implementation of fuzzy PI gain scheduling controller (FPICGS) for controlling flexible joint robot arms with uncertainties from time-varying load. The term FPICGS is called based on a combination of fuzzy PI control scheme with a set of rule bases. Principle of design for a FPICGS is given along with the implementation of the designed computer aided control system. The experiment reveals an effectiveness of the proposed control scheme for flexible joint robot arms driven by a DC motorhooked with a spring which both parameters are completely unknown parameters ...

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.10
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• pp.3038-3047
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• 1996

In telerobot systems, visual servoing of a task object for a slave arm with an eye-in-hand has drawn an interesting attention. As such a task ingenerally conducted in an unstructured environment, it is very difficult to define the inverse feature Jacobian matrix. To overcome this difficulty, this paper proposes an auto-tuning fuzzy rule-based visual servo algorithm. In this algorithm, a visual servo controller composed of fuzzy rules, receives feature errors as inputs and generates the change of have position as outputs. The fuzzy rules are tuned by using steepest gradient method of the cost function, which is defined as a quadratic function of feature errors. Since the fuzzy rules are tuned automatically, this method can be applied to the visual servoing of a slave arm in real time. The effctiveness of the proposed algorithm is verified through a series of simulations and experiments. The results show that through the learning procedure, the slave arm and track object in real time with reasonable accuracy.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.11
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• pp.1-10
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• 1996

In this paper, we suggest a new identification method based on the takagi-sugeno fuzzy model which prepresents an envrionmental stiffness and propose a method to decide PD gains of the PD controller. It is difficult to perform a compliance task due to characteristics of robot itself and uncertain work envronment. Therefore, in this paper, we identify the fuzzy rule by dividing the relationship of input-output data into several piecewise-linear equations using the hough transform which is the one this fuzzy model, we propose a method to design the pD gain. We show the validity of this method by the experiment of tracking the surface of the paper box as an example of variable environment using robot manipulator and force sensing system. As a performance index, we use the settling time, and perform an analysis between conventional PD contorllers and this controller.