Real-Time Identification of Time-Varying Cable Force Using an Improved Adaptive Extended Kalman Filter

Yang, Ning; Li, Jun; Xu, Mingqiang; Wang, Shuqing

doi:10.3390/s22114212

Cited by 9 publications

(5 citation statements)

References 35 publications

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“…As a precondition for a valid assessment of time-varying reliability, recent studies have made significant advancements in the identification of cable forces in stay cables [17,18]. The vibration frequency-based method (VFM) is commonly utilized due to its simplicity, speed, and cost-effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Unveiling Structural Secrets: Active Learning for Assessing Ultimate Load Capacity in Parallel Wire Cable Systems under Time-Varying Force Identification with Frequency-Squeezing Processing and Vibration Frequency Method

Lu,

Li,

et al. 2024

Buildings

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One of the primary challenges in cable-stayed bridges is to assess the service performance of stay cables in response to applied loads and ensure that they meet safety requirements. This paper proposes a new strategy to analyze the time-varying reliability of the ultimate load-carrying capacity of stay cables under resistance and stress uncertainty conditions. Initially, we employ the frequency-squeezing processing (FSP) technique within the vibration frequency method (VFM) to enhance the accuracy and effectiveness of cable force identification through field measurement. Subsequently, we thoroughly discuss and establish the statistical characteristics and probabilistic models of stress, including both slow-varying trend and fast-varying trend components, as well as resistance considering the strengthening deterioration effect. The slow-varying trends of the cable forces are extracted using the moving average method (MAM), and both the extracted slow variation and the fast-varying trend components are analyzed in detail. Finally, we introduce a Gaussian process-based surrogate model to assess the time-varying structural reliability by analyzing the associated limit-state function for the ultimate load capacity of the stay cables. In this study, the proposed strategy is applied to quantify the ultimate load-carrying reliability of a stay cable under the uncertainty of the coupled action of corrosion and fatigue. Compared with conventional reliability analysis, the failure probability interval estimation shows the uncertainty boundaries and provides specific years of reliability failure, which can serve as an important reference for bridge maintenance and strengthening.

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Section: Introductionmentioning

confidence: 99%

Unveiling Structural Secrets: Active Learning for Assessing Ultimate Load Capacity in Parallel Wire Cable Systems under Time-Varying Force Identification with Frequency-Squeezing Processing and Vibration Frequency Method

Lu,

Li,

et al. 2024

Buildings

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show abstract

“…To enhance the convergence, Yu et al [24] proposed a modifed EKF for identifying time-variant Hammerstein-Wiener nonlinear systems. More recently, the KF-based online identifcation of cable force [25], precast segmental columns [26], or sensor fault [27] was also conducted.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Identification of Time-Varying Parameters and External Loads Based on Extended Kalman Filter: Approach and Validation

Zhang

Hua

et al. 2023

Structural Control and Health Monitoring

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Online identification of time-variant parameters without knowledge of external loads is an important but challenging task for structural health monitoring and vibration control. In this study, a two-stage approach, named extended Kalman filter with forgetting factor matrix under unknown inputs (EKF-FFM-UI), is proposed for simultaneously identifying the time-variant parameters and external loads. In stage 1, an extended Kalman filter under unknown inputs (EKF-UI) approach previously proposed by the authors is employed for estimating the structural states and unknown loads. This EKF-UI approach is solely suitable for time-invariant system identification. Therefore, the aim of stage 2 is to improve this approach for the purpose of possessing tracking capability. In this stage, the acceleration responses are first reconstructed by using the differential equation of motion and employed for improving the accuracy of estimated structural states. A forgetting factor matrix is introduced into the priori estimation error covariance matrix to track time-varying parameters. The square errors between the measurements and the corresponding estimates are defined as an index and used for detecting the damage time instant. Then, a covariance resetting technique is employed to assure that such changes in structural parameters can be efficiently captured. A shear-type building structure without/with magneto-rheological (MR) dampers and a fixed beam structure are used as numerical examples for validating the effectiveness of the proposed approach. Experimental tests on a six-story building model are also conducted. Results show the time-varying parameters and unknown inputs can be simultaneously identified with acceptable accuracy.

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“…Among the most relevant modifications are the Extended Kalman filter (EKF), which can be used in non-linear systems by conducting a system linearization; the Unscented Kalman filter (UKF), which is based on the premise that it is easier to approximate a probability distribution than an arbitrary linear transformation, using the Unscented Transformation (UT) for this purpose [3]. However, some modifications based on the adaptability feature, such as the Adaptive Extended Kalman filter (AEKF) and Adaptive Unscented Kalman filter (AUKF), have also been developed to determine the statistical parameters of the dynamic system according to the behavior of the system during data processing [4]. Furthermore, filters for countering undesired responses such as inconsistency from the EKF have been created.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Parameter Identification of a Real Manipulator Robot

Urrea

Agramonte

2022

Symmetry

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Given the widespread use of the Kalman filter in robotics, an increasing number of researchers devote themselves to its study and application. This work underscores the importance of this filter while analyzing the modifications made to the same to improve its performance and reduce its deficiencies in some fields and presenting some of its applications in robotics. The following methods are presented in this study: least squares (LS), Hopfield Neural Networks (HNN), Extended Kalman filter (EKF), and Unscented Kalman filter (UKF). These methods are used in the parameter identification of a Selective Compliant Assembly Robot Arm (SCARA) robot with 3-Degrees of Freedom (3-DoF) and a clamp at its end. The dynamic model of this robot is obtained and employed to identify its parameters; then, the identification results are compared considering the difference between the obtained parameters and the real values of the robot parameters; in this comparison, the good results yielded by the LS and UKF method stand out. Subsequently, the obtained parameters through each method are validated by measuring different performance indexes—during trajectory tracking—such as: Residual Mean Square Error (RMSE), Integral of the Absolute Error (IAE), and the Integral of the Square Error (ISE). In this way, a comparison of the robot’s performance is possible.

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Real-Time Identification of Time-Varying Cable Force Using an Improved Adaptive Extended Kalman Filter

Cited by 9 publications

References 35 publications

Unveiling Structural Secrets: Active Learning for Assessing Ultimate Load Capacity in Parallel Wire Cable Systems under Time-Varying Force Identification with Frequency-Squeezing Processing and Vibration Frequency Method

Unveiling Structural Secrets: Active Learning for Assessing Ultimate Load Capacity in Parallel Wire Cable Systems under Time-Varying Force Identification with Frequency-Squeezing Processing and Vibration Frequency Method

Simultaneous Identification of Time-Varying Parameters and External Loads Based on Extended Kalman Filter: Approach and Validation

Evaluation of Parameter Identification of a Real Manipulator Robot

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