Payload swing control of a tower crane using a neural network–based input shaper

This paper proposes an input shaping technique for efficient payload swing control of a tower crane with cable length variations. Artificial neural network is utilized to design a zero vibration derivative shaper that can be updated according to different cable lengths as the natural frequency and damping ratio of the system changes. Unlike the conventional input shapers that are designed based on a fixed frequency, the proposed technique can predict and update the optimal shaper parameters according to the new cable length and natural frequency. Performance of the proposed technique is evaluated by conducting experiments on a laboratory tower crane with cable length variations and under simultaneous tangential and radial crane motions. The shaper is shown to be robust and provides low payload oscillation with up to 40% variations in the natural frequency. With a 40% decrease in the natural frequency, the superiority of the artificial neural network–zero vibration derivative shaper is confirmed by achieving at least a 50% reduction in the overall and residual payload oscillations when compared to the robust zero vibration derivative and extra insensitive shapers designed based on the average operating frequency. It is envisaged that the proposed shaper can be further utilized for control of tower cranes with more parameter uncertainties.

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Pattern recognition of wood structure design parameters under external interference based on artificial neural network with BIM environment

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189268 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8723-8729

Author(s):

Zheng Qiuling ◽

Yang Ke ◽

Xu Qiang ◽

Zhang Chenglong ◽

Wang Liguang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Damping Ratio ◽

Timber Structure ◽

Design Parameters ◽

Dynamic Parameters ◽

Wood Structure ◽

External Interference ◽

Building Timber ◽

Artificial Neural

Under the influence of novel corona virus pneumonia, the staff are controlled. Therefore, it is a difficult problem to measure the parameters of wood structure building on site. The measurement error of traditional wood structure parameters in complex environment is large, so an efficient and accurate measurement and recognition method is needed. In this paper, a method combining random decrement method and ITD method is proposed to measure the frequency, damping ratio and other structural dynamic parameters of ancient building timber structure under crowd random load excitation. In this paper, the frequency and damping ratio of the typical ancient building timber structure are predicted by using the artificial neural network model trained by the known data. The experimental results show that the population density has a great influence on the measurement of the dynamic parameters of the wooden structure of ancient buildings. Using this method, combined with the long-term monitoring data of temperature and humidity, the influence of various environmental factors on the dynamic characteristics of the structure can be analyzed. This provides data support for structural damage identification and health monitoring.

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Identification of Debonding Damage at Spar Cap-Shear Web Joints by Artificial Neural Network Using Natural Frequency Relevant Key Features of Composite Wind Turbine Blades

Applied Sciences ◽

10.3390/app11125327 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5327

Author(s):

Yun-Jung Jang ◽

Hyeong-Jin Kim ◽

Hak-Geun Kim ◽

Ki-Weon Kang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Wind Turbine ◽

Natural Frequency ◽

Structural Integrity ◽

Natural Frequencies ◽

Turbine Blades ◽

Wind Turbine Blades ◽

Artificial Neural ◽

The Relationship

As the size and weight of blades increase with the recent trend toward larger wind turbines, it is important to ensure the structural integrity of the blades. For this reason, the blade consists of an upper and lower skin that receives the load directly, a shear web that supports the two skins, and a spar cap that connects the skin and the shear web. Loads generated during the operation of the wind turbine can cause debonding damage on the spar cap-shear web joints. This may change the structural stiffness of the blade and lead to a lack of integrity; therefore, it would be beneficial to be able to identify possible damage in advance. In this paper we present a model to identify debonding damage based on natural frequency. This was carried out by modeling 1105 different debonding damages, which were classified by configuration type, location, and length. After that, the natural frequencies, due to the debonding damage of the blades, were obtained through modal analysis using FE analysis. Finally, an artificial neural network was used to study the relationship between debonding damage and the natural frequencies.

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Uncertainty Quantification in Natural Frequency of Composite Plates - An Artificial Neural Network Based Approach

Advanced Composites Letters ◽

10.1177/096369351602500203 ◽

2016 ◽

Vol 25 (2) ◽

pp. 096369351602500 ◽

Cited By ~ 28

Author(s):

Sudip Dey ◽

Tanmoy Mukhopadhyay ◽

Axel Spickenheuer ◽

Uwe Gohs ◽

S. Adhikari

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Uncertainty Quantification ◽

Natural Frequency ◽

Composite Plates ◽

Ann Model ◽

Artificial Neural ◽

Natural Frequency Analysis

This paper presents the stochastic natural frequency for laminated composite plates by using artificial neural network (ANN) model. The ANN model is employed as a surrogate and is trained by using Latin hypercube sampling. Subsequently the stochastic first two natural frequencies are quantified with ANN based uncertainty quantification algorithm. The convergence of the proposed algorithm for stochastic natural frequency analysis of composite plates is verified and validated with original finite element method (FEM) in conjunction with Monte Carlo simulation. Both individual and combined variation of stochastic input parameters are considered to address the influence on the output of interest. The sample size and computational cost are reduced by employing the present approach compared to traditional Monte Carlo simulation.

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