• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.3
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• pp.132-138
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• 2011

This paper presents an intelligent control approach for lateral position control of an autonomous four wheel steered snowplowing robotic vehicle. The vehicle is built for removing snow on the highway. Dynamics of the vehicle is derived and linearized for LQR control. Lateral position is controlled by the LQR method first, then the neural network control technique is introduced to improve tracking performances under the presence of load. The feasibility of using four wheel steering control is investigated by simulation studies of lateral position tracking of the Ford F-250 truck model. Performances of a LQR control method and a neural network control method under virtual snowplowing situation are compared.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.211-214
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• 2002

In this paper, we propose a robust indirect adaptive fuzzy state feedback regulator based on Takagi-Sugeno fuzzy model. The proposed adaptive fuzzy regulator is less sensitive to singularity than the conventional one based on the feedback linearization method. Furthermore, the proposed control method is applicable to not only plants with a perfect model but also plants with an imperfect model, which causes uncertainties. We verify the global stability of the proposed method by using Lyapunov method. In order to support the achievement, the application of the proposed adaptive fuzzy regulator to the control of a nonlinear system under the external disturbance is presented and the performance was verified by some simulation result.

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

• Proceedings of the IEEK Conference
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• 2003.07d
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• pp.1561-1564
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• 2003

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 802.l1b technologies, lots of remote control methods through internet have been proposed. To control a mobile robot through internet and guide it under unknown environment. The information about the direction and velocity of the mobile robot feedbacks to the PDA and the PDA send new control method produced from the combination of Neuro and Hierarchical Fuzzy Algorithm

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.6
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• pp.765-770
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• 2005

This paper presents a new intelligent digital redesign (IDR) method via the compensated bilinear transformation to design the digital controller such that the digital fuzzy system is equivalent to the analog fuzzy system in the sense of the state-matching. This paper especially consider a multirate control scheme with a predictive feature, where the digital control input is held constant N times between the sampling points. More precisely, the multirate control scheme is proposed that utilizes a numerical integration scheme to approximately predict the current state from the state measured at the sampling points, the delayed measurements. For this system, the IDR conditions incorporated with stabilizability in the format of the linear matrix inequalities (LMIs) are derived. The superiority of the proposed technique is convincingly visualized through a numerical example.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.3
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• pp.351-361
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• 2021

This study proposes an intelligent control of manufacturing robot with dual arm based on neural network for smart factory implementation. In the control method of robot system, the perspectron structure of single layer based on neural network is useful for simple computation. However, the limitations of computation are emerging in areas that require complex computations. To overcome limitation of complex parameters computation a new intelligent control technology is proposed in this study. The performance is illustrated by simulation and experiments for manufacturing robot dual arm robot with eight axes.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.70.5-70
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• 2001

This paper proposes a new Internet based control concept & design method and implementation of LonWorks network system for remote intelligent control. The experimental network system using i.LON Web server is designed and fabricated. It is also verified that the developed control modules with LonTalk protocol have available, interoperable, and reliable performance characteristics from the experimental results. Especially, the results provide a available technical data for remote home, building & plant automation control.

• Journal of Power Electronics
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• v.12 no.4
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• pp.587-594
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• 2012

When a PWM rectifier has a low DC-link voltage during startup, the output voltage vector cannot be high enough to regulate the input current. This lack of a PWM rectifier output voltage vector can cause an unregulated inrush current when the rectifier operation starts. This paper presents a PWM rectifier start-up current control algorithm for when it starts operation with a lower DC-link voltage than unloaded condition case. To avoid the unregulated inrush current caused by a lack of DC-link voltage, the proposed control scheme regulates the one phase current with one switch chopping and it generates the current command considering the uncontrolled current magnitude information, which is calculated in advance. Simulation and experiment results support the validity of the proposed method.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.155-159
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• 1995

A design method that can easily construct intelligent patient monitoring systems is proposed. To achieve the design method, the SES/MB concept and a discrete event-based logic control formalism based on a set theory is introduced. In this control paradigm the controller expects to receive confirming sensor responses to its control commands within definite time windows determined by DEVS model of the system under control. Because data to be used for rule-based symbolic reasoning are to be abstracted, several AI methods are applied the processes. These methods are applied to intelligent patient monitoring systems so that they facilitate transformation from low level raw data to high level linguistic data. Model-based system representations have advantages of reusability, extensibility, flexsibility, independent testability and encapsulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.57-62
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• 1998

This paper presents an auto tuning method of PID controller based on the application of fuzzy logic. The proposed method combined the principles of PID control with fuzzy control, which cam considerably improve the performance index of PID controller. Simulation results show that higher performance and accuracy of overall system for desired value is achieved with our manner when compared to widely-used conventional tuning method.