• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.107-112
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• 2005

This paper deals with a fault detection system design for uncertain nonlinear systems modelled as T-S fuzzy systems with the integral quadratic constraints. In order to generate a residual signal, we used a left coprime factorization of the T-S fuzzy system. From the filtered signal of the residual generator, the fault occurence can be detected effectively. A simulation study with nuclear steam generator level control system shows that the suggested method can be applied to detect the fault in actual applications.

• Journal of Korea Spatial Information System Society
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• v.8 no.3
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• pp.117-130
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• 2006

With the rapid development of technologies related to Ubiquitous Sensor Network (USN), sensors are being utilized in various application areas. In general, the data sensed by each sensor node on ubiquitous sensor networks are stored into the central server for efficient search. Because update is delayed to reduce the cost of update in this environment, uncertain data can be stored in the central server. In addition, Uncertain data make query processing produce wrong results in the central server. Thus, this paper examines how to process uncertain data in ubiquitous sensor networks and suggests a new index for efficient processing of uncertain data. The index reduces the cost of update by delaying update in uncertainty areas. Uncertainty areas are areas where uncertain data are likely to exist. In addition, it solves the problem of low accuracy in search resulting from update delay by delaying update only for specific update areas. Lastly, we analyze the performance of the index and prove the superiority of its performance by comparing its performance evaluation.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.9
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• pp.944-955
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• 1990

A high-performance robotic controller is proposed for uncertain robots by using an adaptive control method, which guarantees the boundedness of uncertain systems with partially known uncertainty bounds. In order to improve the tracking performance of the robotic controller, a linear compensator is introduced to the robotic system which has been linearized via a nonlinear feedback. The above adaptive method is then utilized to guarantee the ultimate boundedness of the tracking errors. The performance of the robotic controller is compared with that of the computed torque method by computer simulations under uncertain environments. The simulation results show that the proposed method gives better performance than the computed torque method. Since the proposed method has a small number of parameters to be estimated, the controller is simpler to implement than other existing adaptive controller for robots. Hence, the proposed robotic control method is expected to be well suited for high-performance operation of robots under uncertain environment.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1477-1480
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• 1997

This paper is concerned with the design of a suboptimal robust Kalman filter using LMI approach for system models in the state space, which are subjected to parameter uncertainties in both the state and measurement atrices. Under the assumption that augmented system composed of the uncertain system and the state estimation error dynamics should be stable, a Lyapunov inequality is obtained. And from this inequaltiy, the filter design problem can be transformed to the gneric LMI problems i.e., linear objective minimization problem and generalized eigenvalue minimization problem. When applied to uncertain linear system modles, the proposed filter can provide the minimum upper bound of the estimation error variance for all admissible parameter uncertainties.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1853-1858
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• 2004

This paper presents the development of a full order sliding mode controller for tracking problem of a class of uncertain dynamical system, in particular, the direct drive robot manipulators. By treating the arm as an uncertain system represented by its nominal and bounded parametric uncertainties, a new robust fullorder sliding mode tracking controller is derived such that the actual trajectory tracks the desired trajectory as closely as possible despite the non-linearities and input couplings present in the system. A proportional-integral sliding surface is chosen to ensure the stability of overall dynamics during the entire period i.e. the reaching phase and the sliding phase. Application to a three DOF direct drive robot manipulator is considered.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.3
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• pp.600-603
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• 2007

We propose a method to estimate the asymptotic stability region(ASR) of a mismatched uncertain variable structure system with a bounded controller. The uncertain system under consideration may have mismatched parameter uncertainties in the state matrix. Using linear matrix inequalities(LMIs) we estimate the ASR and we show the quadratic stability of the closed-loop control system in the estimated ASR. We also give a simple LMI-based algorithm for estimating the ASR. Finally, we give a numerical example in order to show the effectiveness of our method.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1210-1215
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• 1990

This paper deals with the fault diagnosis problem in uncertain linear systems having undermodelling, linearization errors and noise inputs. The new approach proposed in this paper uses an appropriate test variable and the difference between system parameters which are estimated by the least squares method to locate the fault. The singular value decomposion is used to decouple the correlation between the estimated system parameters and to observe the trend of parameter changes. Some simulations applied to aircraft ergines show good allocation of the fault even though the system model has significant uncertainties. The feature of the approach is to diagnose the uncertain system through simple parameter operations and not to need complex calculations in the diagnosis procedure as compared with other methods.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.620-623
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• 1997

This paper is concerned with the design of a suboptimal Kalman filter with robust state estimation performance for system models represented in the state space, which are subjected to parameter uncertainties in both the state and measurement matrices. Under the assumption that the uncertain system is quadratically stable, if the augmented system composed of the uncertain system and the filter is controllable, the proposed filter can provide the upper bound of the estimation error variance for all admissible uncertain parameters. This upper bound can be represented as the convex function of a parameter introduced in the design procedure, and the optimized upper bound of the estimation error variance can also be found via the optimization of this convex function.

• 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 Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.191-194
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• 2000

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