• The Journal of Asian Finance, Economics and Business
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• 제5권1호
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• pp.11-16
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• 2018

This research examines the alternative ways of estimating the coefficient of non-diversifiable risk, namely beta coefficient, in Capital Asset Pricing Model (CAPM) introduced by Sharpe (1964) that is an essential element of assessing the value of diverse assets. The non-parametric methods used in this research are the robust Least Trimmed Square (LTS) and Maximum likelihood type of M-estimator (MM-estimator). The Jackknife, the resampling technique, is also employed to validate the results. According to finance literature and common practices, these coecients have often been estimated using Ordinary Least Square (LS) regression method and monthly return data set. The empirical results of this research pointed out that the robust Least Trimmed Square (LTS) and Maximum likelihood type of M-estimator (MM-estimator) performed much better than Ordinary Least Square (LS) in terms of eciency for large-cap stocks trading actively in the United States markets. Interestingly, the empirical results also showed that daily return data would give more accurate estimation than monthly return data in both Ordinary Least Square (LS) and robust Least Trimmed Square (LTS) and Maximum likelihood type of M-estimator (MM-estimator) regressions.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.435-438
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• 1990

A new robust stabilizing method of uncertain system is proposed. The model uncertainty is considered to be the system matrix perturbations. The region of perturbed system eigenvalues are estimated by union of the disks which have the constant radius. Full state feedback control matrix which satisfies the new stabilization condition can be obtained by weighted LQ regulator or pole assignment technique.

• 전기학회논문지
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• 제61권2호
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• pp.284-290
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• 2012

This paper addresses a new passive target tracking problem using the range differences measured by cooperative UAVs. In order to solve the range difference based passive target tracking problem within the framework of linear robust state estimation, the uncertain linear measurement model which contains the stochastic parameter uncertainty is derived by using the noisy range difference measurements. To cope with the performance degradation due to the stochastic parameter uncertainty, the recently developed non-conservative robust Kalman filtering technique [1] is applied. For the cruciform formation flying UAVs, the relationship between the target tracking performance and the measurement errors is quantitatively analyzed. The proposed filter has practical advantages over the classical nonlinear filters because, for its recursive linear structure, it can provide satisfactory convergence properties and is suitable for real-time multiple UAVs applications. Through the simulations, the usefulness of the proposed method is demonstrated.

• 산업경영시스템학회지
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• 제21권48호
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• pp.65-71
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• 1998

Control chart is a very extensively used tool in testing whether a process is in a state of statistical control or not. In this paper, a robust control chart for variables is proposed, which is based on the Huber's M-estimator. The Huber's M-estimator is a well-known robust estimator in sense of distributional robustness. In the proposed chart, the estimation of the process deviation is modified to have a stable level and high power. To compare the performances of the proposed control chart with the classical (equation omitted), some Monte Carlo simulations are performed. The simulation results show that the robust control chart has good performance.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.1055-1058
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• 2003

The robustness and reliability of vision algorithms is the key issue in robotic research and industrial applications. In this paper robust real time visual tracking in complex scene is considered. A common approach to increase robustness of a tracking system is the use of different model (CAD model etc.) known a priori. Also fusion or multiple features facilitates robust detection and tracking of objects in scenes of realistic complexity. Voting-based fusion of cues is adapted. In voting. a very simple or no model is used for fusion. The approach for this algorithm is tested in a 3D Cartesian robot which tracks a toy vehicle moving along 3D rail, and the Kalman filter is used to estimate the motion parameters. namely the system state vector of moving object with unknown dynamics. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• International Journal of Control, Automation, and Systems
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• 제1권4호
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• pp.520-526
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• 2003

In the case of a strapdown inertial navigation system (SDINS) with sizeable attitude errors, the uncertainty caused by linearization of the system degrades the performance of the filter. In this paper, a robust filter and various error models for the uncertainty are presented. The analytical characteristics of the proposed filter are also investigated. The results show that the filter does not require the statistical property of the system disturbance and that the region of the estimation error depends on a freedom parameter in the worst case. Then, the uncertainty of the SDINS is derived. Depending on the choice of the reference frame and the attitude error state, several error models are presented. Finally, various in-flight alignment methods are proposed by combining the robust filter with the error models. Simulation results demonstrate that the proposed filter effectively improves the performance.

• 전기학회논문지
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• 제57권7호
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• pp.1247-1253
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• 2008

A design method of a T-S fuzzy model based reduced order nonlinear unknown input observer(NUIO) is presented. The fuzzy NUIO is designed based on the parallel distributed compensation(PDC) concept. It consists of a number of the linear UIOs, each of which is designed for each local linear model in the T-S fuzzy model of a class of nonlinear systems. The fuzzy NUIO provides not only the state estimates insensitive to the unknown inputs, for example, disturbances and faults etc., but also the estimates of the unknown inputs. Therefore, It can be employed in the state feedback control and disturbance rejection control of a class of nonlinear systems with unknown disturbances. It also applied to the robust residual generation for the fault detection and isolation systems and to the design of fault tolerant control systems. As an example, the NUIO is applied to an inverted pendulum system to show the state and disturbance estimation performance and to illustrate the fuzzy reduced order NUIO design method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 A
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• pp.187-190
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• 2003

In this paper, we address the robust adaptive backstepping controller using fuzzy neural network (FHIN) for a class of uncertain output feedback nonlinear systems with disturbance. A new algorithm is proposed for estimation of unknown bounds and adaptive control of the uncertain nonlinear systems. The state estimation is solved using K-fillers. All unknown nonlinear functions are approximated by FNN. The FNN weight adaptation rule is derived from Lyapunov stability analysis and guarantees that the adapted weight error and tracking error are bounded. The compensated controller is designed to compensate the FNN approximation error and external disturbance. Finally, simulation results show that the proposed controller can achieve favorable tracking performance and robustness with regard to unknown function and external disturbance.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 추계학술대회논문집
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• pp.115-120
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• 2004

This paper concerns the active aeroelastic control of flapped wing systems exposed to blast and/or the sonic boom in an incompressible flow field. This is achieved via implementation of a robust estimation capability (sliding mode observer: SMO), and of the use of the deflected flap as to suppress the flutter instability or enhance the subcritical aeroelastic response to blast loads. To this end, a control methodology using LQG(Linear Quadratic Gaussian) in conjunction with SMO is implemented, and its performance toward suppressing flutter and reducing the vibrational level in the subcritical flight speed range is demonstrated. Moreover, its performances are compared to the ones provided via implementation of conventional LQG with Kalman filter.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.328-333
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• 1996

In this paper, we propose a new method to estimate robot position without landmark. At first, it is studied to estimate robot state using Markov decision rule. And, a matching method is discussed for estimating current position more accurately under the estimated current state. At second, we combine or fuse the matching method with the POMDP method in order to estimate the position under a dynamically changing environment. Finally we will show that our method can estimate the position precisely and robustly of which error are not cumulated through simulation results.