• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2319-2324
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• 2005

This paper proposes a intelligent gain and boundary layer based sliding mode control (SMC) method for robotic systems with unknown model uncertainties. For intelligent gain and boundary layer, we employ the self recurrent wavelet neural network (SRWNN) which has the properties such as a simple structure and fast convergence. In our control structure, the SRWNNs are used for estimating the width of boundary layer, uncertainty bound, and nonlinear terms of robotic systems. The adaptation laws for all parameters of SRWNNs and reconstruction error bounds are derived from the Lyapunov stability theorem, which are used for an on-line control of robotic systems with unknown uncertainties. Accordingly, the proposed method can overcome the chattering phenomena in the control effort and has the robustness regardless of unknown uncertainties. Finally, simulation results for the three-link manipulator, one of the robotic systems, are included to illustrate the effectiveness of the proposed method.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.37-40
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• 2002

Intelligent Wearable Module is intelligent system that arises when a human is part of the feedback loop of a computational process like a certain control system. Applied system is mobile robot. This paper represents the mobile robot control system remote controlled by Intelligent Wearable Module. So far, owing to the development of internet technologies, lots of remote control methods through internet have been proposed. To control a mobile robot through internet and guide it under unknown environment, We propose a control method activated by Intelligent Wearable Module. In a proposed system, PDA acts as a user interface to communicate with notebook as a controller of the mobile robot system using TCP/IP protocol, and the notebook controls the mobile robot system. Tlle information about the direction and velocity of the mobile robot feedbacks to the PDA and the PDA send new control method produced from the fuzzy inference engine.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.154-161
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• 2012

This article presents experimental studies of fuzzy logic application to control a two-wheel mobile robot(TWMR) system. The TWMR system is composed of two systems, an inverted pendulum system and a mobile robot system. Although linear controllers can stabilize the TWMR, fuzzy controllers are expected to have robustness to uncertainties so that the resulting performances are expected to be better. Nominal fuzzy rules are used to control balance and position of TWMR. Fuzzy logic is embedded on a DSP chip to control the TWMR. Balancing performances of the PID controller and the fuzzy controller under disturbances are compared through extensive experimental studies.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.4
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• pp.31-38
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• 2022

In this study, we designed and manufactured an intelligent gripper-control device to safely grip unknown objects. The gripper control device consists of a DSP circuit, power supply circuit, communication circuit, and amplifier circuit diagrams. The DSP is used because the values of the 3-axis force sensor to which the gripper is attached are measured and calculated at high speeds. The gripping force is determined based on this value, and the object must be safely gripped with the determined value. A basic characteristic test of the control device of the manufactured intelligent gripper was conducted, and it was confirmed that it operated safely.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2784-2786
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• 2000

Mobile robots include control modules for autonomous obstacle avoidance and navigation. They are range modules to detect and avoid obstacles. motor control modules to operate two wheels. and encoder modules for localization. There is needed an appropriate controller for each modules. In this paper. a control system. including 18 channels for Sonar sensors. 4 channels for PWM modules. and 4 channels for encoder modules. is proposed using TMS320C32 DSP adopted with CAN. The board communicates with other modules by CAN. so that mobile robots can perform several tasks in real time. So we can realize on autonomous mobile robot with basic functions such as obstacle avoidance by using the developed controller. Especially. this controller has 100 msec scan time for 16 sonar sensors and can detect closer objects comparing with standard sonar sensors.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.99-102
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• 2001

It has well known that the congestion of traffic and it's distribution. There are very important problems in the traffic control systems. In this paper, we will purpose an ITFS(Intelligent Traffic Forecasting System) which can determine the car classes and transport them to ITS(Intelligent Traffic control System). The system will be used the Inductive Loop Detector(ILD)and the Fuzzy logic and shown the effectiveness by the computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.146.2-146
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• 2001

Recently, a need of non-industrial robot, such as service, medical, entertainment and house-keeping robot, has been increased. Therefore, the capability of robot which can do intelligent behavior like interaction with men and its environment become more prominent than the capability of executing simple repetitive task. To implement an intelligent robot which provides intelligent behavior, an effective system architecture including perception, learning, reasoning and action part is necessary. Control architectures for intelligent robot can be divided into two different classes. One is deliberative type controller which is applicate to high level intelligence like environment ...

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.451-456
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• 2005

In this paper, we propose the navigation control method for intelligent robot using messy genetic algorithm. The fuzzy controller design for navigation of the intelligent robot was dependant on expert's knowledge. But, the parameters of the fuzzy logic controller obtained from expert's control action may not be outimal. In this paper, to solve the above problem, we propose the identification method to automatically tune the number of fuzzy rule and parameters of memberships of fuzzy controller using mGA. Finally, to show and evaluate the generality and feasibility of the proposed method, we provides some simulations for wall following navigation of intelligent robot.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.133-136
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• 2001

This paper partially presents a Hierachical neural network architecture for providing the intelligent control of complex Automatic Transmission(AJT) system which is usually nonlinear and hard to model mathematically. It consists of the module to apply or release an engine brake at the slope and that to judge the intention of the driver. The HNN architecture simplifies the structure of the overall system and is efficient for the learning time. This paper describes how the sub-neural networks of each module have been constructed and will compare the result of the intelligent hJT control to that of the conventional shift pattern.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.325-329
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• 2003

This paper discuss the method of the system's efficient control using a Intelligent hybrid algorithm in nonlinear dynamics systems. Existing neural network and genetic algorithm for the control of non-linear systems work well in static states. but it be not particularly good in changeable states and must re-learn for the control of the system in the changed state. This time spend a lot of time. For the solution of this problem we suggest the intelligent hybrid self-tuning controller. it includes neural network, genetic algorithm and immune system. it is based on neural network, and immune system and genetic algorithm are added against a changed factor. We will call a change factor an antigen. When an antigen broke out, immune system come into action and genetic algorithm search an antibody. So the system is controled more stably and rapidly. Moreover, The Genetic algorithm use the memory address of the immune bank as a genetic factor. So it brings an advantage which the realization of a hardware easy.