Multi-parameter decoupling and slope tracking control strategy of a large-scale high altitude environment simulation test cabin

Li, Ké; Liu, Wangkai; Wang, Jun; Huang, Yong; Li, Meng

doi:10.1016/j.cja.2014.10.005

Cited by 13 publications

(5 citation statements)

References 10 publications

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“…Usually, DBN has more hidden layers than the BP network, and the network parameters of the DBN are initialized layer by layer [ 16 , 17 ]. The DBN network can not only solve a complex nonlinear problem but also extract more features from high-dimensional data.…”

Section: Feature Extraction and Classification Algorithmmentioning

confidence: 99%

Multi-label spacecraft electrical signal classification method based on DBN and random forest

et al. 2017

PLoS ONE

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In spacecraft electrical signal characteristic data, there exists a large amount of data with high-dimensional features, a high computational complexity degree, and a low rate of identification problems, which causes great difficulty in fault diagnosis of spacecraft electronic load systems. This paper proposes a feature extraction method that is based on deep belief networks (DBN) and a classification method that is based on the random forest (RF) algorithm; The proposed algorithm mainly employs a multi-layer neural network to reduce the dimension of the original data, and then, classification is applied. Firstly, we use the method of wavelet denoising, which was used to pre-process the data. Secondly, the deep belief network is used to reduce the feature dimension and improve the rate of classification for the electrical characteristics data. Finally, we used the random forest algorithm to classify the data and comparing it with other algorithms. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in terms of accuracy, computational efficiency, and stability in addressing spacecraft electrical signal data.

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Section: Feature Extraction and Classification Algorithmmentioning

confidence: 99%

Multi-label spacecraft electrical signal classification method based on DBN and random forest

et al. 2017

PLoS ONE

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“…Gravity method is used to solve the fuzzy calculation to get the output of the controller by the control rule. 21 The output of the fuzzy controller can be calculated by the equation (14).…”

Section: Fuzzy Pid Controller Based On Rekfmentioning

confidence: 99%

Precision tension control technology of composite fiber tape winding molding

Zhang

Tang

Shi

2017

Journal of Thermoplastic Composite Materials

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During the composite fiber tape winding process, tension fluctuation has a serious impact on the performance parameters of winding products, such as strength, compactness, resin content, fatigue resistance, and stress uniformity. Because of the influence of dynamic performance of tension control system caused by the torque motor, external disturbance, sensitivity of the parameter variation, inverter dead zone, ripple torque, cogging torque, nonlinear friction, and measurement noise, a fuzzy proportion integral derivative (PID) controller based on robust extended Kalman filter (REKF) is proposed, which considers membership function optimization of nonlinear dynamic fuzzy control system as systematic identification through designating activated membership function of input and output variable in each control step, and then, online adjustment of the activated membership functions is used to reduce the control error by REKF. By adjusting the shape and position of membership function, the designed controller can be adapted to the dynamic conditions. Simulation and experimental results show that overall performance of the control system has been significantly improved. Compared with traditional fuzzy PID, the fuzzy PID controller based on REKF has strong anti-interference performance and robustness, and the winding tension control precision has been increased by 40–42%.

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“…28 Many specific troubleshooting cases for spacecraft were presented for developing a hybrid reasoning mechanism based on rule-based reasoning, and also a novel mathematical model of thermal equilibrium prediction was proposed for simulating the temperature anomalies in thermal balance test failures due to the emergence of solar wings for troubleshooting. 29,30…”

Section: Introductionmentioning

confidence: 99%

A novel method for spacecraft electrical fault detection based on FCM clustering and WPSVM classification with PCA feature extraction

Song

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part G:

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The measurement of spacecraft electrical characteristics and multi-label classification issues are generally including a large amount of unlabeled test data processing, high-dimensional feature redundancy, time-consumed computation, and identification of slow rate. In this paper, a fuzzy c-means offline (FCM) clustering algorithm and the approximate weighted proximal support vector machine (WPSVM) online recognition approach have been proposed to reduce the feature size and improve the speed of classification of electrical characteristics in the spacecraft. In addition, the main component analysis for the complex signals based on the principal component feature extraction is used for the feature selection process. The data capture contribution approach by using thresholds is furthermore applied to resolve the selection problem of the principal component analysis (PCA), which effectively guarantees the validity and consistency of the data. Experimental results indicate that the proposed approach in this paper can obtain better fault diagnosis results of the spacecraft electrical characteristics' data, improve the accuracy of identification, and shorten the computing time with high efficiency.

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Multi-parameter decoupling and slope tracking control strategy of a large-scale high altitude environment simulation test cabin

Cited by 13 publications

References 10 publications

Multi-label spacecraft electrical signal classification method based on DBN and random forest

Multi-label spacecraft electrical signal classification method based on DBN and random forest

Precision tension control technology of composite fiber tape winding molding

A novel method for spacecraft electrical fault detection based on FCM clustering and WPSVM classification with PCA feature extraction

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