• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.769-772
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• 1993

This paper presents and adaptive fuzzy controller using fuzzy neural networks(FNNs). The adaptive controller uses two FNNs. One FNN is used to identify a fuzzy model of controlled object. The other FNN is used as a fuzzy controller. The fuzzy controller is designed with the linguistic rules of the fuzzy model. The response of the designed control system is checked with a linguistic response analysis proposed by the authors. An adaptive tuning of the control rules of the FNN controller is made possible utilizing the fuzzy model. Simulations using nonlinear controlled objects were done to verify the proposed control system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1199-1202
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• 1993

An Optimum Fuzzy Controller which can be sued to direct the driver to control a running train in an optimum operation way has been developed. In the development process of the controller, the theory and technology of Optimum Control and Fuzzy Control are applied. Practical field tests have been carried out in P.R. of China. In order to make the function of the controller more perfect, the controller is improved by the advanced fuzzy control technology and tool in Japan. The computer simulation of the improved controller has been finished.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.4
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• pp.67-76
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• 1995

In recent research on the fuzzy controller, the crisp type fuzzy controller model, in which the consequent part of the fuzzy control rules are crisp real numbers instead of fuzzy sets, due to its simplicity in calculation, has been widely used in various applications. In this paper we try to analyze the dynamical behavior of the crisp type fuzzy controller with both inference methods of min-max compositional rule and product-sum inference. The analysis reveals that a crisp type fuzzy controller behaves approximately like a PD controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.463-471
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• 1999

This paper presents a vector controlled induction motor is implemented by neural networks system compared with PI controller for the speed control. The design employed the training strategy with Neural Network Controller(NNC) and Neural Network Emulator(NNE) for speed. In order to update the weights of the controller First of all Emulator updates its parameters by identifying the motor input and output next it supplies the error path to the output stage of the controller using backpropagation algorithm, As Controller produces an adequate output to the system due to neural networks learning capability Vector controlled induction motor characteristics actual motor speed with based on neural network system follows the reference speed better than that of linear PI speed controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.143-153
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• 1997

At the on-line control method FLC(Fuzzy Logic Controller) is stronger to the disturbance than a classical controller and its overshoot of the initialized value is excellent. The fuzzy controller can do a proper control, though it doesn't know the mathematical model of the system or the parameter value. But to make the control rule of the fuzzy controller through an expert's experiance has a changes of the control system, the control rule is fixed, it can't adjust to the environment changes of the control system, the controller output value has a minute error and it can't convergence correctly to the desired value[1][2]. There are many ways to eliminate the minute error[3][4][5], but in this paper suggests EP-FNNIC(Fuzzy Neurla Network Intelligence Controller) intelligence controller which combines FLC with NN(Neural Network) and EP(Evolution Programming). The output characteristics of EP-FNNIC controller will be compared and analyzed with FLC. It will be showed that this EP-FN IC controller converge correctly to the desirable value without any error. The convergence speed, overshoot, rising time, error of steady state of controller of these two kinds also will be compared.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.568-576
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• 1998

In this thesis discuss with a flexible robotic finger design and controller which is used for the micro flexible robotic finger. So, miniaturization, precision, controller for the control of grasping force and actuator were needed. And, even if we develop a new actuator and controller, in order to use on real system, we must considerate of a many side problem. In a force control of micro flexible finger for grasping an object, the fingertip's vibration was more important task of accuracy control. And, controller were adopt the PD/PI mixed type fuzzy controller. The controller were consist of two part, one is a PD type fuzzy controller for increase the rising time response, the other is a PI type fuzzy controller for decrease of steady-state error. Especially, in a PD type fuzzy controller, we used only seven rules. And, for a PI controller, we adopt a reset factor for the control of input values. so, we have overcome the exceed of controller's input range. For the estimate of ontroller's utility and usefulness, we have experiment and computer simulation of three cases. First, we consider of unit force grasping control for a task object, which is 0.03N. Second, bounding grasping force control which is add to a sinusoidal force on the unit force. At this cases the task force is (0.03+0.01 sin wt N). And consider of following of rectangular forces.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.8
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• pp.126-133
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• 2000

This paper is a study on robust PID controller tuning technique using the frequency region model matching method.To design the robust PID controller satisfying disturbance attenuation and robust tracking property for a reference input first an {{{{ETA _$\infty$}}}} controller satisfying given performances is designed using an H$_{\infty}$ control method, And then the parameters(proportional gain integral gain and derivation gain) of the robust PID controller with the performances of the desinged H$_{\infty}$ controller are determined using the model matching method at frequency domain. in this paper this PID controller tuning technique is applied to PID speed controller design for BLDC motors. Consequently simulation results show that the proposed PID speed controller satisfies load torque disturbance attenuation and robust tracking property and this study has usefulness and applicability for the speed control system; design of BLDC motors.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.199-202
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• 1998

This paper describe an intelligent cruise control system for automobile. With the remarkable numerical increase of automobiles on the road, the optimized traffic flow control using the cruise control is one of the very important traffic problems to overcome the limitation of an existing road capacity. Based on this idea that minimize the fuel cost and the air pollution, and accept a driver's needs for driving, we have developed an intelligent cruise control system for vehicle. This proposed intelligent fuzzy cruise controller was successfully implemented using the fuzzy algorithm, the i80c196 μ-controller board and the throttle valve actuator. The field test results on an linear road was introduced.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2266-2270
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• 2005

In this paper, an internet based control application for dynamical systems is implemented. This implementation is maily targeted for the part of advanced control education. Intelligent control algorithms are implemented in a PC so that a client can remotely access the PC to control a dynamical system through the internet. Neural network is used as an on-line intelligent controller. To have on-line learning and control capability, the reference compensation technique is implemented as intelligent control hardware of combining a DSP board and an FPGA chip. GUIs for a user are also developed for the user's convenience. Actual experiments of motion control of a DC motor have been conducted to show the performance of the intelligent control though the internet and the feasibility of advanced control education.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.318-323
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• 2005

In this paper, we propose a systematic method of a fuzzy-model-based controller for continuous-time nonlinear dynamical systems which may contain uncertainties. The continuous-time uncertain TS fuzzy model is first constructed to represent the uncertain nonlinear system. A parallel distributed compensation (PDC) technique is then used to design a fuzzy model based controller for both stabilization and tracking. Finally, the designed continuous-time controller is converted to an equivalent discrete-time controller by using an intelligent digital redesign method. This new design technique provides a systematic and effective framework for integration of the fuzzy model based control theory and the advanced digital redesign technique for nonlinear dynamical systems with uncertainties. Finally, the single link flexible-joint robot arm is used as an illustrative example to show the effectiveness and the feasibility of the developed design method.