• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.261-269
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• 2018

In this paper, we propose a Takagi-Sugeno (T-S) fuzzy-model-based design for the tracking control of a class of nonlinear underwater glider. By using the partial linearization and the sector nonlinearity, the underwater glider with six degrees of freedom (6 DOF) is modelled by the T-S fuzzy model. The concerned tracking control problem with $H_{\infty}$ performance is converted into the stabilization one for the error dynamics between the given nonlinear underwater glider and the reference time-varying input. Sufficient conditions are derived for the asymptotic stabilizability of the error dynamics in the format of matrix inequality. Simulation results demonstrate the effectiveness of the proposed design methodology.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.15-17
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• 2001

This paper deals with a design problem of a switching-type fuzzy-model-based controller for a nonlinear system. Takagi-Sugeno(TS) fuzzy model and duffing forced-oscillation system are employed in designing the switching-type fuzzy controller. Finally, we analyze the stability of the global system controlled by the proposed controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.117-120
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• 2001

This paper presents a new global state-matching intelligent digital redesign method for nonlinear systems by using genetic algorithms (GAs). The proposed method results in global matching of the states of the analogously controlled system with those of the digitally controlled system while the conventional intelligent digital redesign method does not. The proposed method provides a new approach for the digital redesign of a class of fuzzy-model-based controllers.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2037-2045
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• 2016

This paper presents a fuzzy-model-based design approach to the buoyancy and moment controls of a class of nonlinear underwater glider. Through the linearization and the sector nonlinearity methodologies, the underwater glider dynamics is represented by a Takagi-Sugeno (T-S) fuzzy model. Sufficient conditions are derived to guarantee the asymptotic stability of the closed-loop system in the format of linear matrix inequality (LMI). Simulation results demonstrate the effectiveness of the proposed buoyancy and moment controllers for the underwater glider.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2630-2632
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• 2000

In this paper, a control technique of Takagi-Sugeno (TS) fuzzy systems with parametric uncertainties is developed. The uncertain TS fuzzy system is represented as an uncertain multiple linear system. The control problem of TS fuzzy system is converted into the stabilization problem of a uncertain multiple linear system. A sufficient condition for robust stabilization is obtained in terms of linear matrix inequalities (LMI). A Design example is illustrated to show the effectiveness of the proposed method.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.1276-1281
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• 2006

In general, the wheel-skid prevention of braking system is very important in modern railway applications. This is because wheel-skid can lead to an increase in noise and vibration from wheels with flat points, as well as an increased braking distance. However conventional anti-skid control has problems because wheel adhension and skid characteristics are very difficult nonlinear systems and time consuming to accurately model. In this paper, we design a fuzzy controller using a model of relation between ahdension and braking force, we show that anti-skid fuzzy controller has a very good performance, performing better than the previous conventional controller.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4B
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• pp.566-575
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• 2010

In recent, artificial immune system has become an important research direction in the anomaly detection of networks. The conventional artificial immune systems are usually based on the negative selection that is one of the computational models of self/nonself discrimination. A main problem with self and non-self discrimination is the determination of the frontier between self and non-self. It causes false positive and false negative which are wrong detections. Therefore, additional functions are needed in order to detect potential anomaly while identifying abnormal behavior from analogous symptoms. In this paper, we design novel network attack detection and response schemes based on artificial immune system, and evaluate the performance of the proposed schemes. We firstly generate detector set and design detection and response modules through adopting the interaction between dendritic cells and T-cells. With the sequence of buffer occupancy, a set of detectors is generated by negative selection. The detection module detects the network anomaly with a set of detectors and generates alarm signal to the response module. In order to reduce wrong detections, we also utilize the fuzzy number theory that infers the degree of threat. The degree of threat is calculated by monitoring the number of alarm signals and the intensity of alarm occurrence. The response module sends the control signal to attackers to limit the attack traffic.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.211-217
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• 1998

Many practical control problems for the complex, uncertain or large-scale plants, need to simultaneously achieve a number of objectives, which may conflict or compete with each other. If the conventional optimization methods are applied to solve these control problems, the solution process may be time-consuming and the resulting solution would ofter lose its original meaning of optimality. Nevertheless, the human operators usually performs satisfactory results based on their qualitative and heuristic knowledge. In this paper, we investigate the control strategies of the human operators, and propose a fuzzy model-based multi-objective satisfactory controller. We also apply it to the automatic train operation(ATO) system for the magnetically levitated vehicles(MAGLEV). One of the human operator's strategies is to predict the control result in order to find the meaningful solution. In this paper, Takagi-Sugeno fuzzy model is used to simulated the prediction procedure. Another str tegy is to evaluate the multiple objectives with respect to their own standards. To realize this strategy, we propose the concept of a satisfactory solution and a satisfactory control scheme. The MAGLEV train is a typical example of the uncertain, complex and large-scale plants. Moreover, the ATO system has to satisfy multiple objectives, such as seed pattern tracking, stop gap accuracy, safety and riding comfort. In this paper, the speed pattern tracking controller and the automatic stop controller of the ATO system is designed based on the proposed control scheme. The effectiveness of the ATO system based on the proposed scheme is shown by the experiments with a rotary test bed and a real MAGLEV train.