• Smart Structures and Systems
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• 제12권6호
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• pp.619-640
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• 2013

It is highly desirable to explore efficient algorithms for detecting structural damage of large size structural systems with limited input and output measurements. In this paper, a new structural damage detection algorithm based on substructure approach is proposed for large size structural systems with limited input and output measurements. Inter-connection effect between adjacent substructures is treated as 'additional unknown inputs' to substructures. Extended state vector of each substructure and its unknown excitations are estimated by sequential extended Kalman estimator and least-squares estimation, respectively. It is shown that the 'additional unknown inputs' can be estimated by the algorithm without the measurements on the substructure interface DOFs, which is superior to previous substructural identification approaches. Also, structural parameters and unknown excitation are estimated in a sequential manner, which simplifies the identification problem compared with other existing work. Structural damage can be detected from the degradation of the identified substructural element stiffness values. The performances of the proposed algorithm are demonstrated by several numerical examples and a lab experiment. Measurement noise effect is considered. Both the simulation results and experimental data validate that the proposed algorithm is viable for structural damage detection of large size structural systems with limited input and output measurements.

• Structural Engineering and Mechanics
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• 제63권6호
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• pp.779-788
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• 2017

System identification and damage detection for structural health monitoring have received considerable attention. Various time domain analysis methodologies based on measured vibration data of structures have been proposed. Among them, recursive least-squares estimation of structural parameters which is also known as parametric Kalman filter (PKF) approach has been studied. However, the conventional PKF requires that all the external excitations (inputs) be available. On the other hand, structural uncertainties are inevitable for civil infrastructures, it is necessary to develop approaches for probabilistic damage detection of structures. In this paper, a parametric Kalman filter with unknown inputs (PKF-UI) is proposed for the simultaneous identification of structural parameters and the unmeasured external inputs. Analytical recursive formulations of the proposed PKF-UI are derived based on the conventional PKF. Two scenarios of linear observation equations and nonlinear observation equations are discussed, respectively. Such a straightforward derivation of PKF-UI is not available in the literature. Then, the proposed PKF-UI is utilized for probabilistic damage detection of structures by considering the uncertainties of structural parameters. Structural damage index and the damage probability are derived from the statistical values of the identified structural parameters of intact and damaged structure. Some numerical examples are used to validate the proposed method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 추계학술대회 논문집 학회본부
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• pp.386-389
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• 1991

The problem of constructing an bilinear observer for use in the control of superheater temperature with desuperheater is considered. The distributed heat input into the superheater is usually not available for use in the observer, and hence is treated as an unknown inputs. The bilinear observer theory for system with unknown inputs is exploited and applied to the problem.

• 한국항행학회논문지
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• 제2권1호
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• pp.34-42
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• 1998

임의로 변하는 미지의 입력을 갖는 표적의 추적을 위한 적용 상태 및 입력 추정기를 설계한다. 미지의 입력을 semi-Markov 프로세스로 모델링하고, 이를 Bayesian 추정이론에 접목함으로써 여러개의 Kalman 필터가 병렬로 구성된 효과적인 적용 상태 및 입력 추정기를 구한다. 컴퓨터 모사를 통하여, 제안된 적응추정기는 임의로 변하는 미지의 입력에도 불구하고 개선된 추적성능을 보임을 확인하였다.

• 한국정밀공학회지
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• 제11권6호
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• pp.20-27
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• 1994

This paper presents a controller design to coordinate a robot manipulator under unknown system parameters and bounded disturbance inputs. To control the motion of the manipulator, an inverse dynamics control scheme is applied. Since parameters of the robot manipulators such as mass and inertia are not perfectly known, the difference between the actual and estimated parameters works as a disturbance force. To identify the unknown parameters, an improved adaptive control algorithm is directly derived from a chosen Lyapunov's function candidate based on the Lyapunov's Second Method. A robust control algorithm is devised to counteract the bounded disturbance inputs such as contact forces and disturbing forces coming from the difference between the actual and the estimated system parameters. Numerical examples are shown using three degree-of-freedom planar arm.

• Smart Structures and Systems
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• 제24권2호
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• pp.209-221
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• 2019

The classical Kalman filter (KF) provides a practical and efficient way for state estimation. It is, however, not applicable when the external excitations applied to the structures are unknown. Moreover, it is known the classical KF is only suitable for linear systems and can't handle the nonlinear cases. The aim of this paper is to extend the classical KF approach to circumvent the aforementioned limitations for the joint estimation of structural states and the unknown inputs. On the basis of the scheme of the classical KF, analytical recursive solution of an improved KF approach is derived and presented. A revised form of observation equation is obtained basing on a projection matrix. The structural states and the unknown inputs are then simultaneously estimated with limited measurements in linear or nonlinear systems. The efficiency and accuracy of the proposed approach is verified via a five-story shear building, a simply supported beam, and three sorts of nonlinear hysteretic structures. The shaking table tests of a five-story building structure are also employed for the validation of the robustness of the proposed approach. Numerical and experimental results show that the proposed approach can not only satisfactorily estimate structural states, but also identify unknown loadings with acceptable accuracy for both linear and nonlinear systems.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2611-2613
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• 2000

This paper deals with a novel approach to unknown inputs observer(UIO) design for linear time-invariant dynamical systems using a fast Walsh transform and Walsh function's differential operation. Generally, UIO has a derivation of system outputs which is not available from the measurement directly. And it is an obstacle to estimate the unknown inputs properly when unexpected measurement noises are presented. Therefore, this paper propose an algebraic approach to eliminate such problems by using a Walsh function's differential operation.

• 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 2000년도 제15차 학술회의논문집
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• pp.452-452
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• 2000

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 magnitude of failures are detected and isolated easily by using integrated output error under the step failures. Finally, a numerical example is given to verify the effectiveness of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.156-160
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• 1993

This paper presents a controller design to coordinate a robot manipulator under unknown system parameters and bounded disturbance inputs. To control the motion of the manipulator, an inverse dynamics control scheme is applied. Since parameters of the robot manipulators such as mass and inertia are not perfectly known, the difference between the actual and estimated parameters works as a disturbance force. To identify the unknown parameters, an inproved adaptive control algorithm is directly derived from a chosen Lyapunov's function candidate based on the Lyapunov's Second Method. A robust control algorithm is devised to counteract the bounded disturbance inputs such as contact forces and disturbing force coming from the difference between th actual and the estimated system parameters. Numerical examples are shown using three degree-of-freedom planar arm.