• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.111-118
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• 2016

Artificial intelligence (AI) is making computer systems intelligent to do right thing. The AI is used today in a variety of fields, such as journalism, medical, industry as well as entertainment. The impact of AI is becoming larger day after day. In general, the AI system has to lead the optimal decision under uncertainty. But it is difficult for the AI system can derive the best conclusion. In addition, we have a trouble to represent the intelligent capacity of AI in numeric values. Statistics has the ability to quantify the uncertainty by two approaches of frequentist and Bayesian. So in this paper, we propose a methodology of the connection between statistics and AI efficiently. We compute a fixed value for estimating the population parameter using the frequentist learning. Also we find a probability distribution to estimate the parameter of conceptual population using Bayesian learning. To show how our proposed research could be applied to practical domain, we collect the patent big data related to Apple company, and we make the AI more intelligent to understand Apple's technology.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.1
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• pp.63-73
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• 2014

For robust stability analysis of parameters uncertainty missiles, the traditional frequency domain method can only analyze each respective channel at several interval points within uncertain parameter space. Discontinuous calculation and couplings between channels will lead to inaccurate analysis results. A method based on the ${\nu}$-gap metric is proposed, which is able to comprehensively evaluate the robust stability of missiles with uncertain parameters; and then a genetic-simulated annealing hybrid optimization algorithm, which has global and local searching ability, is used to search for a parameters combination that leads to the worst stability within the space of uncertain parameters. Finally, the proposed method is used to analyze the robust stability of a re-entry missile with uncertain parameters; the results verify the feasibility and accuracy of the method.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1850-1851
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• 2006

The sliding mode control is acceptable for Autonomous Underwater Vehicle(AUV), since the dynamics of AUV are highly nonlinear and have several parameter uncertainty such as the added mass terms, the hydrodynamic coefficients. The sliding mode control can deal well with nonlinearity of the system and offers a robustness to controller with parameter uncertainty. Since sliding mode control has the defect of chattering problem, only in ideal case the actuator can respond by control law. Therefore we propose the sliding mode control with non-chattering. And computer simulations illustrate the performance of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.431-441
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• 1991

A robust H$_{\infty}$ control design methodology and its application to a Space Station attitude and momentum control problem are presented. This new approach incorporates nonlinear multi-parameter variations in the state-space formulation of H$_{\infty}$ control theory. An application of this robust H$_{\infty}$ control synthesis technique to the Space Station control problem yields a remarkable result in stability robustness with respect to the moments-of-inertia variation of about 73% in one of the structured uncertainty directions. The performance and stability of this new robust H$_{\infty}$ controller for the Space Station are compared to those of other controllers designed using a standard linear-quadratic-regulator synthesis technique.que.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.193-196
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• 1992

When there exist parameter uncertainty, modelling errors and nonminimum phase zeros in control object system. the stability robustness of conventional LQG and LOG/LTR methods are not satisfactory[2, 8]. Since these methods are performed on the infinite horizon, it is very hard to establish exact design parameters and thus they have lots of problems to be applied to real systems, So in this paper we propose RHLQG/FIRF optimal controller which has robust stability against parameter uncertainty, nonminimum phase zeros and modelling errors. This method uses only the information around at present and therefore shows good performance even when we do not know exact design parameters. We here compare LQG and LQG/LTR method with RHLQG/FIRF controller and exemplify that RHLQG/FIRF controller has better robust stability performance via simulations.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.4
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• pp.255-261
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• 2000

This paper presents the results of the robust H(sub)$\infty$ FIR filtering for a class of nonlinear continuous time-varying systems subject to real norm-bounded parameter uncertainty and know Lipschitz nonlinearity under sampled measurements. We address the problem of designing filters, using sampled measurements, which guarantee a prescribed H(sub)$\infty$ performance in continuous time-varying context, irrespective of the parameter uncertainty and unknown initial states. The infinite horizon causal H(sub)$\infty$FIR filter are investigated using the finite moving horizon in terms of two Riccati equations with finite discrete jumps.

• Journal of Advanced Marine Engineering and Technology
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• v.17 no.5
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• pp.63-70
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• 1993

In 1980's to 1990's the marine propulsion diesel engines have been developed into lower speed and longer stroke for the energy saving(small S.F.O.C.). As these new trends the convetional mechnical-hydraulic governors were not adapted to the new requirements and the digital governors have been adopted in the marine use. The digital governors usually use the control algorithms such as the PID control, optimal control, adaptive control and etc. While the engine has delay time and parameter variations these control algorithms have difficulty in considering the stability and the robustness for the model uncertainty. In this study, the $H_{\infty}$ controller design method are applied to the speed control of the low speed marine diesel engine. By comparison the $H_{\infty}$ control results with the PID control results, the validity of the $H_{\infty}$ controller under the delay time and parameter variations is confirmed.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.102-107
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• 2009

The Possibilistic C-means(PCM) was proposed to overcome some of the drawbacks associated with the Fuzzy C-means(FCM) such as improved performance for noise data. However, PCM possesses some drawbacks such as sensitivity in initial parameter values and to patterns that have relatively short distances between the prototypes. To overcome these drawbacks, we propose an interval type 2 fuzzy approach to PCM by considering uncertainty in the fuzzy parameter m in the PCM algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.8
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• pp.1429-1433
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• 2008

In this paper, we consider the design method of robust guaranteed cost controller for discrete-time singular systems with norm-bounded time-varying parameter uncertainty. In order to get the optimum(minimum) value of guaranteed cost, an optimization problem is given by linear matrix inequality (LMI) approach. The sufficient condition for the existence of controller and the upper bound of guaranteed cost function are proposed in terms of strict LMIs without decompositions of system matrices. Numerical examples are provided to show the validity of the presented method.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.139-142
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• 1997

In this paper, a robust controller is proposed to achieve an accurate tracking for an uncertain nonlinear plant with actuator dynamics. The extent of parameter uncertainty can be quantified by using linear parameterization technique. A switching controller is proposed to guarantee the global asymptotic stability of the plant. In order to eliminate the chattering caused by the switching controller, a smoothing controller is designed using the boundary layer technique around the sliding surface and guarantees the uniform ultimate boundedness of the tracking error.