A health performance evaluation method of multirotors under wind turbulence

Zhao, Zhiyao; Wang, Xiaoyi; Yao, Ping; Bai, Yuting

doi:10.1007/s11071-020-06041-3

Cited by 50 publications

(30 citation statements)

References 37 publications

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“…When

λ = 1

, the FFSG algorithm reduces to the SG algorithm in (13)–(15). The FFSG parameter estimation method for a class of nonlinear systems in this article can combine some mathematical tools 45‐48 to study the parameter estimation problems of different systems with colored noises 49‐53 and can be applied to other literatures such as signal modeling and communication networked systems and engineering application systems 54‐60 and so forth.…”

Section: The Ffsg Algorithmmentioning

confidence: 99%

Three‐stage forgetting factor stochastic gradient parameter estimation methods for a class of nonlinear systems

Kang

2020

Intl J Robust & Nonlinear

164

109

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This article focuses on the parameter estimation for a class of nonlinear systems, that is, multi‐input single‐output or two‐input single‐output Hammerstein finite impulse response systems with autoregressive moving average noise. The key is to investigate new estimation methods for on‐line parameter estimation of the considered system. By using the gradient search and introducing the forgetting factor, the forgetting factor stochastic gradient estimation method is developed. For the purpose of improving the parameter estimation accuracy, the system is decomposed into three subsystems with fewer variables applying the key term separation technique: the first two subsystems contain the unknown parameters related to the input and the third subsystem contains the unknown parameters related to the noise. Then a three‐stage forgetting factor stochastic gradient algorithm is proposed based on the hierarchical identification principle for interactively identifying each subsystem. The simulation results show the effectiveness of the presented algorithm.

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“…When

λ = 1

Section: The Ffsg Algorithmmentioning

confidence: 99%

Three‐stage forgetting factor stochastic gradient parameter estimation methods for a class of nonlinear systems

Kang

2020

Intl J Robust & Nonlinear

164

109

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“…The UAV123 [ 16 ] dataset contains a total of 123 video sequences from an aerial viewpoint. To demonstrate the performance of proposed tracker, some state-of-the-art trackers, including ECO [ 27 ], CSR_DCF [ 27 ], SRDCF [ 13 ], Staple [ 26 ], DSST [ 10 ] and LCT [ 34 ] are used to compare with our tracker. It should be noted that only single-object tracking task is considered in the comparison of this paper.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…The experimental parameters are described in Table 2 . The parameters related to the long term filter

and the scale filter

are selected referring to the LCT [ 34 ] and DSST [ 10 ] trackers. The parameters in Table 2 are chosen based on the ECO [ 27 ] and CSR_DCF [ 35 ] trackers and fine-tuned according to the tracking AUC performance.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…However, these strategies, which make the tracker lose the continuous information related to the appearance change of target and produce over-fitting to the current state of the target, deteriorate the tracking performance, more seriously, lead to tracking failure. Some trackers [ 34 , 35 , 36 ] rectified the target position by conducting an accurate dynamic model of the moving target, however, non-linear characteristic of the dynamic model is hard to describe. Several other methods, such as the LCT [ 37 ] tracker, used the re-detection module to retrieve the target from tracking failures.…”

Section: Introductionmentioning

confidence: 99%

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Robust Visual Tracking Based on Adaptive Multi-Feature Fusion Using the Tracking Reliability Criterion

Zhou

Wang

Jin

et al. 2020

Sensors

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Multi-resolution feature fusion DCF (Discriminative Correlation Filter) methods have significantly advanced the object tracking performance. However, careless choice and fusion of sample features make the algorithm susceptible to interference, leading to tracking failure. Some trackers embed the re-detection module to remedy tracking failures, yet distinguishing ability and stability of the sample features are scarcely considered when training the detector, resulting in low effectiveness detection. Firstly, this paper proposes a criterion of feature tracking reliability and conduct a novel feature adaptive fusion framework. The feature tracking reliability criterion is proposed to evaluate the robustness and distinguishing ability of the sample features. Secondly, a re-detection module is proposed to further avoid tracking failures and increase the accuracy of target re-detection. The re-detection module consists of multiple SVM detectors trained by different sample features. When the tracking fails, the SVM detector trained by the most reliable sample feature will be activated to recover the target and adjust the target position. Finally, comparison experiments on OTB2015 and UAV123 databases demonstrate the accuracy and robustness of the proposed method.

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“…The real-time online Fault Detection and Identification (FDI) of the abnormal state of the quadcopter is vital for the safe flight of aircraft. The current available methods for Unmanned Aerial Vehicle (UAV) fault diagnosis can be basically divided into three categories: analytical model-based methods, knowledge-based methods, and signal processing-based methods [ 8 , 9 , 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Identification Method for Quadcopter Based on Airframe Vibration Signals

Zhang

Zhao

Wang

et al. 2021

Sensors

Self Cite

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Quadcopters are widely used in a variety of military and civilian mission scenarios. Real-time online detection of the abnormal state of the quadcopter is vital to the safety of aircraft. Existing data-driven fault detection methods generally usually require numerous sensors to collect data. However, quadcopter airframe space is limited. A large number of sensors cannot be loaded, meaning that it is difficult to use additional sensors to capture fault signals for quadcopters. In this paper, without additional sensors, a Fault Detection and Identification (FDI) method for quadcopter blades based on airframe vibration signals is proposed using the airborne acceleration sensor. This method integrates multi-axis data information and effectively detects and identifies quadcopter blade faults through Long and Short-Term Memory (LSTM) network models. Through flight experiments, the quadcopter triaxial accelerometer data are collected for airframe vibration signals at first. Then, the wavelet packet decomposition method is employed to extract data features, and the standard deviations of the wavelet packet coefficients are employed to form the feature vector. Finally, the LSTM-based FDI model is constructed for quadcopter blade FDI. The results show that the method can effectively detect and identify quadcopter blade faults with a better FDI performance and a higher model accuracy compared with the Back Propagation (BP) neural network-based FDI model.

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A health performance evaluation method of multirotors under wind turbulence

Cited by 50 publications

References 37 publications

Three‐stage forgetting factor stochastic gradient parameter estimation methods for a class of nonlinear systems

Three‐stage forgetting factor stochastic gradient parameter estimation methods for a class of nonlinear systems

Robust Visual Tracking Based on Adaptive Multi-Feature Fusion Using the Tracking Reliability Criterion

Fault Detection and Identification Method for Quadcopter Based on Airframe Vibration Signals

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