scholarly journals Online Dynamic Load Identification Based on Extended Kalman Filter for Structures with Varying Parameters

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1372
Author(s):  
Hongqiu Li ◽  
Jinhui Jiang ◽  
M Shadi Mohamed
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Dynamic Load ◽  
Degrees Of Freedom ◽  
Symmetric System ◽  
Identification Algorithm ◽  
Structure Parameters ◽  
Load Identification ◽  
Unknown Parameters ◽  
Varying Parameters

Dynamic load identification is an inverse problem concerned with finding the load applied on a structure when the dynamic characteristics and the response of the structure are known. In engineering applications, some of the structure parameters such as the mass or the stiffness may be unknown and/or may change in time. In this paper, an online dynamic load identification algorithm based on an extended Kalman filter is proposed. The algorithm not only identifies the load by measuring the structural response but also identifies the unknown structure parameters and tracks their changes. We discuss the proposed algorithm for the cases when the unknown parameters are the stiffness or the mass coefficients. Furthermore, for a system with many degrees of freedom and to achieve online computations, we implement the model reduction theory. Thus, we reduce the number of degrees of freedom in the resulting symmetric system before applying the proposed extended Kalman filter algorithm. The algorithm is used to recover the dynamic loads in three numerical examples. It is also used to identify the dynamic load in a lab experiment for a structure with varying parameters. The simulations and the experimental results show that the proposed algorithm is effective and can simultaneously identify the parameters and any changes in them as well as the applied dynamic load.

An Algorithm of Dynamic Load Identification Based on FBG Sensor and Kalman Filter

2018 ◽  
Vol 38 (3) ◽  
pp. 0328012
Author(s):  
宋雪刚 Song Xuegang ◽  
刘鹏 Liu Peng ◽  
程竹明 Cheng Zhuming ◽  
魏真 Wei Zhen ◽  
喻俊松 Yu Junsong ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Dynamic Load ◽  
Load Identification ◽  
Fbg Sensor

scholarly journals Ship-to-Ship State Observer Using Sensor Fusion and the Extended Kalman Filter

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Sondre Sanden Tørdal ◽  
Geir Hovland
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Extended Kalman Filter ◽  
Relative Motion ◽  
Degrees Of Freedom ◽  
Load Transfer ◽  
Real Life ◽  
Fusion Algorithm ◽  
Six Degrees Of Freedom ◽  
Motion Estimate

In this paper, a solution for estimating the relative position and orientation between two ships in six degrees-of-freedom (6DOF) using sensor fusion and an extended Kalman filter (EKF) approach is presented. Two different sensor types, based on time-of-flight and inertial measurement principles, were combined to create a reliable and redundant estimate of the relative motion between the ships. An accurate and reliable relative motion estimate is expected to be a key enabler for future ship-to-ship operations, such as autonomous load transfer and handling. The proposed sensor fusion algorithm was tested with real sensors (two motion reference units (MRS) and a laser tracker) and an experimental setup consisting of two Stewart platforms in the Norwegian Motion Laboratory, which represents an approximate scale of 1:10 when compared to real-life ship-to-ship operations.

Parameter identification of a differentiable Bouc-Wen model using constrained extended Kalman filter

2020 ◽  
pp. 147592172092943
Author(s):  
Dan Li ◽  
Yang Wang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Extended Kalman Filter ◽  
Structural Safety ◽  
Model Parameters ◽  
Suitable Model ◽  
Identification Algorithm ◽  
Hysteretic Model ◽  
System Equations ◽  
Parameter Constraints

Hysteresis is of critical importance to structural safety under severe dynamic loading conditions. One of the widely used hysteretic models for civil structures is the Bouc-Wen model, the effectiveness of which depends on suitable model parameters. The locally non-differentiable governing equation of the conventional Bouc-Wen model poses difficulty on existing identification algorithms, especially the extended Kalman filter, which relies on linearized system equations to propagate state estimates and covariance. In addition, the standard extended Kalman filter usually does not incorporate parameter constraints, and therefore may result in unreasonable estimates. In this article, a modified and differentiable Bouc-Wen model, together with a constrained extended Kalman filter (CEKF), is proposed to identify the hysteretic model parameters in a reliable way. The partial derivatives of the differentiable Bouc-Wen model with respect to hysteretic parameters can be easily calculated for implementing the identification algorithm. Constrained extended Kalman filter restricts the Kalman gain to ensure that the estimates of parameters satisfy constraints from physical laws. Parameter identification using simulated and experimental data collected from a four-story structure demonstrates that constrained extended Kalman filter can achieve more reliable identification results than the standard extended Kalman filter.

scholarly journals Load Parameter Identification for Parallel Robot Manipulator Based on Extended Kalman Filter

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shijie Song ◽  
Xiaolin Dai ◽  
Zhangchao Huang ◽  
Dawei Gong
Keyword(s):  
Kalman Filter ◽  
Dynamic Model ◽  
Extended Kalman Filter ◽  
Robot Manipulator ◽  
External Disturbance ◽  
Parallel Robot ◽  
Load Parameter ◽  
Identification Algorithm ◽  
Dynamic Parameters ◽  
Dynamic Coupling

Load is the main external disturbance of a parallel robot manipulator. This disturbance will cause dynamic coupling among different degrees of freedom and make heaps of model-based control methods difficult to apply. In order to compensate this disturbance, it is crucial to obtain an accurate dynamic model of load. However, in practice, the load is always uncertain and its dynamic parameters are arduous to know a priori. To cope with this problem, this paper proposes a novel and simple approach to identify the dynamic parameters of load. Firstly, the dynamic model of the parallel robot manipulator with uncertain load is established and the dynamic coupling caused by load is also analyzed. Then, according to the dynamic model, the excitation signal is designed and a weak nonlinear dynamic model is derived. Furthermore, the identification model is presented and the identification algorithm based on the extended Kalman filter is designed. Lastly, numerical simulation results, obtained using a six-degree-of-freedom Gough–Stewart parallel manipulator, demonstrate the good estimation performance of the proposed method.

Object Identification and Tracking via Noise Updated Iterative Extended Kalman Filter

Volume 1 ◽  
2004 ◽  
Author(s):  
A. Bondarenko ◽  
Y. Halevi ◽  
M. Shpitalni
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Moving Objects ◽  
Mathematical Formulation ◽  
Substantial Improvement ◽  
Object Identification ◽  
State Variables ◽  
Unknown Parameters ◽  
Method Of Solution ◽  
Simultaneous Identification

The paper considers the problem of simultaneous identification and trajectory tracking of moving objects (either 2D or 3D) from moving sensors. The identification is parametric and is based on knowing the family that the object belongs to, e.g. ball, ellipsoid, box, etc. The mathematical formulation results in implicit measurements, i.e. an algebraic equation that includes both state variables and actual measurements. The method of solution is via Extended Kalman Filter where the unknown parameters are regarded as additional state variables. Standard Extended Kalman Filter and Iterative Extended Kalman Filter yielded unsatisfactory results, mainly due to the nonlinearity of the measurements in both the state vector and the noise. A new algorithm, called Noise Updated Iterative Extended Kalman Filter is suggested. Its deviation from the standard iterative Kalman filter is in estimating the measurement noise at each iteration. The estimated noise is then used in the linearization stage to obtain a more accurate linear approximation. The method has been applied to the online identification and tracking problem, with substantial improvement in performance.

scholarly journals Dynamic load identification algorithm based on Newmark-β and self-filtering

2019 ◽  
Vol 234 (1) ◽  
pp. 96-107
Author(s):  
Fan Yuchuan ◽  
Chunyu Zhao ◽  
Yu Hongye ◽  
Bangchun Wen
Keyword(s):  
Dynamic Load ◽  
Recognition Accuracy ◽  
Computation Time ◽  
The Self ◽  
Iteration Algorithm ◽  
Identification Algorithm ◽  
Load Identification ◽  
Filtering Algorithm ◽  
Recognition Result

In this paper, a dynamic load identification iteration algorithm based on Newmark -β is proposed. Aiming at the problem of excessive iteration error in the process of calculation, a self-filtering algorithm is proposed and added to the load identification algorithm. After adding the self-filtering algorithm, the recognition accuracy of the algorithm has been improved significantly. The recognition result of the proposed method and explicit Newmark- β method is compared by simulations and experiment. The results show that the recognition precision and calculation efficiency of this algorithm are higher, especially in the aspect of calculation efficiency, the proposed method has obvious advantages. Under the same conditions, the proposed method can save a lot of computation time.

A modified extended Kalman filter for linear discrete-time systems with unknown parameters

Automatica ◽  
1981 ◽  
Vol 17 (4) ◽  
pp. 657-660 ◽  
Author(s):  
Toshio Yoshimura ◽  
Katsunobu Konishi ◽  
Takashi Soeda
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Discrete Time ◽  
Unknown Parameters ◽  
Discrete Time Systems ◽  
Time Systems

Online identification of impact loads of multi-degree-of-freedom system based on Kalman filter

2020 ◽  
Vol 64 (1-4) ◽  
pp. 359-367
Author(s):  
Jinhui Jiang ◽  
Shuyi Luo ◽  
Zhongzai Liang
Keyword(s):  
Kalman Filter ◽  
Dynamic Load ◽  
Impact Load ◽  
Structural Response ◽  
Degree Of Freedom ◽  
Load Identification ◽  
Theoretical Derivation ◽  
Structural Dynamic ◽  
Online Identification ◽  
Multiple Impact

Dynamic load identification is the second kind of inverse problem in structural dynamics. It is a process of reconstructing load applied to structure in case of structural dynamic model and information of structural response. Online identification is one of the frontier problems in dynamic load identification, which has high difficulty and broad application prospects. In this paper, an online identification of dynamic load of the multi-degree-of-freedom system based on Kalman filter in modal space is proposed. Since the Kalman filter has excellent real-time performance and robustness, it is possible to be used in dynamic load online identification. We start from the theoretical derivation in detail for the multi-degree-of-freedom system, then the feasibility and effectiveness of the method is verified by numerical simulation of three-degree-of-freedom system with the single impact load and continuous multiple impact load.

Parameters Identification Strategy for a Generalized Vehicle Concept Model for Ride and Handling Analysis

2013 ◽  
Author(s):  
Mariano Carpinelli ◽  
Marco Gubitosa ◽  
Domenico Mundo ◽  
Wim Desmet
Keyword(s):  
Degrees Of Freedom ◽  
Early Stage ◽  
Parameters Identification ◽  
Least Square ◽  
Identification Algorithm ◽  
Vehicle Concept ◽  
Unknown Parameters ◽  
Multibody Model ◽  
Concept Model ◽  
Predecessor Model

In this paper we propose a structured approach for the parameters identification of a multibody vehicle concept model to be used for the combined analysis of vertical and longitudinal dynamics. The model here proposed adopts eight degrees of freedom in the space. The wheels are connected to the sprung mass in an equivalent trailing arm configuration thus enabling to reproduce the squat and dive phenomena. This conceptual suspension representation allows determining the dynamic response of the vehicle during longitudinal acceleration or braking maneuvers. The identification procedure here suggested evaluates the unknown parameters of the model, being the global stiffness and damping coefficients of the suspensions and the positions of the pivot points of the trailing arms. The identification algorithm is based on non-linear least square costs that can be computed by having as reference the signals of a measurement campaign which is conducted on a real vehicle as well as on a virtual predecessor model. The results here shown make use of virtually measured quantities coming from ride maneuvers performed by means of a high fidelity multibody model of a passenger car. The presented concept model, showing good correlation with respect to the reference signals, is suggested as a reliable prediction and optimization tool in the early stage of the design phase of new vehicles.

Sign in / Sign up

Export Citation Format

Share Document