Parameter identification for nonlinear time-varying dynamic system based on the assumption of “short time linearly varying” and global constraint optimization

Chen, Tengfei; He, Huan; Chen, Guoping; Zheng, Yuxuan; Hou, Shuo; Xi, Xulong

doi:10.1016/j.ymssp.2020.106620

Cited by 12 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the identification procedure, the interval size is of great significance. [26][27][28] When the interval size is large, more data will be contained in each interval, which can improve the algorithm's robustness. However, the actual parameters will deviate from the initial values to a greater extent, which will cause serious errors.…”

Section: Matching Algorithm Of Interval Size and Different Parametersmentioning

confidence: 99%

“…25 Moreover, rather than being regarded as constants, the parameters in the new assumption of ''short-time linear variation'' introduced by Chen were represented by the linear regression equations separately in each interval and deduced by least-squares (LS) algorithm. 26 With this method, the author successfully estimated the TV parameters of the second-order vibration structure and efficiently improved recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Time-varying structural parameter identification for the offshore gangway

Xu,

Yang,

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

The offshore gangway is a new type of offshore transfer equipment, equivalent to an offshore passage. This device makes it convenient for people to transfer between a vessel and an offshore structure. The control algorithm of the gangway system demands adjusting its control parameters to deal with harsh sea conditions and ensure personnel safety. Thence, it is essential to foreknow its time-varying structural parameters, such as the moment of inertia, the mass, the location of the mass center, and so on. However, due to the highly complex structure, these parameters are difficult to obtain through measurement directly. To adaptively identify these time-varying parameters for better controller design, this paper proposes a novel structural parameter identification approach based on the idea of interval division. During the identification process, the entire period is uniformly divided into several intervals. The time-varying model of the offshore gangway system can be represented in each interval by a quadratic regression equation, the coefficients of which can be derived by the least-squares algorithm. Furthermore, to adapt to the different variation frequencies and amplitudes of the parameters, the divided intervals are of distinct sizes according to the characteristics of each identification parameter. In addition, the particle swarm algorithm is employed to obtain the optimal interval sizes, the cost function of which consists of a series of output error terms. Verified by simulation, the method presented in this paper can effectively estimate the structural parameters of the offshore gangway with high accuracy, with mean square errors reduced by at least 2/3 compared to other methods proposed in previous research.

show abstract

Section: Matching Algorithm Of Interval Size and Different Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Time-varying structural parameter identification for the offshore gangway

Xu,

Yang,

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

show abstract

Identification of linear time-varying dynamic systems based on the WKB method

et al. 2023

View full text Add to dashboard Cite

This work proposes a new parameter identification method based on the Wentzel-Kramers-Brillouin (WKB) approximation for slow linear time-varying (LTV) dynamic systems. The considered time period is divided into a series of short time windows. In each time window, the assumption of “short time linearly varying” parameters is employed, and a nonlinear optimization problem is solved using the WKB results for the slow LTV dynamic system. A search algorithm is developed to find the optimal solution. In the identification process, only one type of response signal (displacement, velocity or acceleration) is required. Thus, numerical differentiation or integration of the measured signal, which leads to truncation or cumulative errors in noise environment, is avoided. The accuracy and robustness of the new identification method are validated by applying it to a particular LTV system with time-varying stiffness.

show abstract

Recursive formulation of the WKB solution for linear time-varying dynamic systems

Chen

et al. 2021

Acta Mech

View full text Add to dashboard Cite

Parameter identification for nonlinear time-varying dynamic system based on the assumption of “short time linearly varying” and global constraint optimization

Cited by 12 publications

References 18 publications

Time-varying structural parameter identification for the offshore gangway

Time-varying structural parameter identification for the offshore gangway

Identification of linear time-varying dynamic systems based on the WKB method

Recursive formulation of the WKB solution for linear time-varying dynamic systems

Contact Info

Product

Resources

About