• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1191-1194
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• 1993

We consider a fuzzy controller corresponding to PI controller. This controller is applied to a controlled object which is a first order lag system with dead time. An antecedent part is divided into 3, 5, and 7 parts ( membership function of triangle shape ), and a consequent part into 3, 5, and 7 parts ( membership function of singleton ). In each combination of an antecedent part and a consequent one. We compare control efficiency under the performance criteria such that the overshoot is kept 20% and the ITAE index is minimized.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1386-1389
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• 1993

We are describing the architecture of a fuzzy logic controller using pulse-width-modulation (PDM) technique and a pipeline structure. Features of this controller are: A new architecture for the inference unit, reduced chip area and less I/O-pins. Additionally we present two different rule-bases: one hardwired with reduced chip-area and the other programmable for prototyping. Also an architecture of a parallel minimum-gate is shown.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1086-1089
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• 1993

The paper deal with the differences between a fuzzy logic controller with a complete linguistic description and one with an incomplete linguistic description. The conditions to get a complete crisp controller by using a fuzzy logic controller with incomplete description are analyzed, and an application to the control of an analog PLL circuit is described, [1].

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.180-183
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• 1996

This work discusses simulation results for the fuzzy logic controller tested the project“Fuzzy Ramp Metering Algorithm Implementation.”The performance objectives were, in order of priority, to maximize total vehicle-miles, maximize mainline speeds, and minimize delay per vehicle while maintaining an acceptable ramp queue. In the fuzzy logic controller, the sensors from the on-ramps were helpful in maintaining reasonable ramp queue and mainline congestion because it considered these factors simultaneously. Each metered ramp had a parameter input file, which allowed the controller to be modified without recompiling the software. Consequently, maintenance costs should be minimal.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1099-1104
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• 2003

In this paper, the position control of a car-like mobile robot using neural network is proposed. The positional information of the mobile robot is given by a laser range finder located remotely through wireless communication. The heading angle is measured by a gyro sensor. Considering these two sensor information as references, the robot posture by localization is corrected by a cascaded controller. In order to improve the tracking performance, a neural network with a cascaded controller is used to compensate for any uncertainty in the robot. The remotely located neural network filter modifies the reference trajectories to minimize the positional errors by wireless communication. A car-like mobile robot is built as a test-bed and experimental studies of proposed several control algorithms are performed. It turns out that the best position control can be achieved by a cascaded controller with neural network.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.4
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• pp.9-17
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• 2019

Sun power generation systems which use large capacity centralized inverters have loss of power generation due to cloud and building shadows, pollution, cell deterioration, etc. To minimize loss of power generation, decentralized solar power systems using multiple micro-inverters are being proposed as an alternative. A distributed solar power system consisting of a system-connected system uses power line communication to collect data from the micro-inverters. Power line communication has the advantage of using power lines without separate lines for data transmission, but in distributed solar power generation systems that use a large number of micro-inverters, the bit error rate is less reliable due to the phenomenon caused by limited transmission power, high load interference and noise, variable signal attenuation, and impedance characteristics. So we proposed wireless intelligent controller for micro-inverter that is used to build distributed solar power systems. and we design and implement that. Further, the proposed wireless intelligent controller for micro-inverter was used to establish a small-volume solar power plant to check its function and operation.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.4
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• pp.49-60
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• 1996

The objective of this paper is to develop an adaptive learning method for fuzzy hypercubes using a neural network. An intelligent control system is proposed by exploiting only the merits of a fuzzy logic controller and a neural network, assuming that we can modify in real time the consequential parts of the rulebase with adaptive learning, and that initial fuzzy control rules are established in a temporarily stable region. We choose the structure of fuzzy hypercubes for the fuzzy controller, and utilize the Perceptron learning rule in order to upda1.e the fuzzy control ru1c:s on-line with the output errors. As a result, the effectiveness and the robustness of this intelligent controller are shown with application of the proposed adaptive fuzzy-neuro controller to control of the cart-pole system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.390-393
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• 2007

In this paper, applications of self recurrent neuro-fuzzy controller to stabilization of nonlinear system are considered. The architecture of self recurrent neuro-fuzzy controller is fix layer, and the hidden layer is comprised of self recurrent architecture. Also, generalized dynamic error-backpropagation algorithm is used for the learning of the self recurrent neuro-fuzzy controller. To demonstrate the efficiency of the self recurrent neuro-fuzzy control algorithm presented in this study, a self recurrent neuro-fuzzy controller was designed and then a comparative analysis was made with LQR controller through an simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.326-329
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• 2003

Controller that is designed in this paper is form that apply PID controller about Fuzzy algorithm. Fuzzy Controller that using this paper is can speak that compensation style fuzzy controller as form to solidify action of PID controller for plant. This is not form that autotuning the each PID coefficient. We Apply and examined the response character to AGC(Automatic Generation Control) system using designed controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.189-193
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• 1998

A PID type fuzzy Controller is proposed based on a crisp type model in which the consequent parts of the fuzzy control rules are functional representation or real numbers. Using the conventional PID control theory, a new PID type fuzzy controller is developed, which retains the characteristics of the conventional PID controller. An advantage of this approach, is that it simplifies the complicated defuzzification algorithm which could be time consuming. Computer simulation results have shown that the proposed PID fuzzy controller has satisfactory tracking performance.