• 제어로봇시스템학회논문지
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• 제20권2호
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• pp.170-174
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• 2014

In this paper, an unknown input estimation method via the optimal FIR smoother is proposed for linear discrete-time systems. The unknown inputs are represented by random walk processes and treated as auxiliary states in augmented state space models. In order to estimate augmented states which include unknown inputs, the optimal FIR smoother is applied to the augmented state space model. Since the optimal FIR smoother is unbiased and independent of any a priori information of the augmented state, the estimates of each unknown input are independent of the initial state and of other unknown inputs. Moreover, the proposed method can be applied to stochastic singular systems, since the optimal FIR smoother is derived without the assumption that the system matrix is nonsingular. A numerical example is given to show the performance of the proposed estimation method.

• International Journal of Control, Automation, and Systems
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• 제3권1호
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• pp.117-121
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• 2005

This paper deals with the state estimation of linear time-invariant discrete systems with unknown inputs. The forward sequences of the output are treated as additional outputs. In this case, the rank condition for designing the unknown input estimator is relaxed. The gain for minimal estimation error variance is presented, and a numerical example is given to verify the proposed unknown input estimator.

• 한국융합학회논문지
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• 제4권4호
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• pp.41-48
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• 2013

Observer design for system with unknown input was carried out. First, Kalman filter was considered to estimate system state with White noise. With the results of Kalman filter design, state observer, controller properties, including controllability and observability, and the Kalman filter structure and algorithm were also studied. Kalman filter algorithm was applied to Position and velocity measurement based on Kalman filter with white noise, and it was constructed and achieved by programming based on Matlab programming. Finally, observer for system with unknown input was constructed with the help of Gershgorin's disc theorem. With the designed observer, system states was constructed and applied to system with unknown input. By simulation results, estimation performance was verified. In this project, state feedback control theory, observer theory and relevant design procedure, as well as Kalman filter design were understood and used in practical application.

• 한국항공우주학회지
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• 제41권4호
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• pp.284-290
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• 2013

위성의 각속도를 추정하고 반작용 휠의 고장을 검출하기 위하여 비선형 미지입력 관측기를 제안하였다. 위성의 각속도 추정을 위해서 입력 값을 필요로 하는데, 구동기에 고장이 발생하는 경우에 정확한 각속도 추정이 어렵다. 미지입력 관측기는 미지의 고장에 영향을 받지 않으면서 상태추정이 가능하다. 본 논문에서는 SDRE 기법을 적용하여 비선형 미지입력 관측기를 설계하였고, 시뮬레이션을 이용하여 제안한 방법을 검증하였다. 다양한 형태로 동시에 고장이 발생하더라도, 각속도 추정 및 고장 검출이 가능하였다.

• 조명전기설비학회논문지
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• 제22권2호
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• pp.101-106
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• 2008

본 논문에서는 측정 잡음이 존재하는 선형시스템의 미지 입력 추정에 관하여 기술하였다. 적분관측기를 기초로 한 본 연구의 방법은 미지 입력을 정확히 추정해낼 수 있을 뿐만 아니라 추정 성능도 높일 수 있다. 시뮬레이션 결과와 기존 방법의 비교를 통해 본 연구 방법의 유용성을 입증할 수 있었다.

• 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.

• Wind and Structures
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• 제17권4호
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• pp.451-464
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• 2013

This study develops an extended inverse input estimation algorithm with intelligent adaptive fuzzy weighting to effectively estimate the unknown input wind load of nonlinear structural systems. This algorithm combines the extended Kalman filter and recursive least squares estimator with intelligent adaptive fuzzy weighting. This study investigated the unknown input wind load applied on a tower structural system. Nonlinear characteristics will exist in various structural systems. The nonlinear characteristics are particularly more obvious when applying larger input wind load. Numerical simulation cases involving different input wind load types are studied in this paper. The simulation results verify the nonlinear characteristics of the structural system. This algorithm is effective in estimating unknown input wind loads.

• 제어로봇시스템학회논문지
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• 제17권11호
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• pp.1138-1144
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• 2011

This article deals with the problem of fault detection of a spacecraft's actuators. The authors introduce an extended unknown input observer for nonlinear systems. This is an extended form of unknown input observers which are used for linear systems. Since faults are not available, those are considered as unknown inputs. Unknown input observers can estimate states without full information of inputs if some conditions are satisfied. The authors suggest a continuous-time extended UIO (eUIO) and prove the convergence of state estimation errors. Since the dynamic equation of a spacecraft is nonlinear, an extended UIO can be applied. Three eUIOs are designed to monitor three reaction wheels. The moving averages of each eUIO's residuals are selected for decision logic. The proposed method is verified by numerical simulations.

• Transactions on Control, Automation and Systems Engineering
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• 제3권2호
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• pp.77-82
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• 2001

This paper deals with a fault detection algorithm for linear descriptor systems via unknown input PI observer. An unknown input PI observer is presented and its realization conditions is proposed by using the rank condition of system matrices. From the characteristics of unknown input PI observer, the states of system with unknown inputs are estimated and the occurrences of fault are detected, and its magnitudes are estimated easily by using integrated output estimation error under the step faults. Finally, a numerical example is given to verify the effectiveness of the proposed fault detection algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1998년도 제13차 학술회의논문집
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• pp.323-328
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• 1998

In this paper, a new input estimation algorithm is proposed for target tracking problem. The unknown target maneuver is approximated by a linear combination of independent time functions and the coefficients are estimated by using a weighted least-squares estimation technique. The proposed algorithm is verified by computer simulation of a realistic two-dimensional tracking problem. The proposed algorithm provides significant improvements in estimation performance over the conventional input estimation techniques based on the constant-input assumption.