Intelligent attitude and flapping angles estimation of flybarless helicopters under near-hover conditions

Al-Sharman, Mohammad; Jaradat, Mohammad A.; Abdel–Hafez, Mamoun F.

doi:10.1016/j.jfranklin.2018.07.003

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…addressed in helicopter system, such as high nonlinearity of model, underactuated with strong coupling, system unmodeled dynamics, externally uncertain interference, noise reduction and energy saving. Although the previous work proposed some control methods [5][6][7][8][9][10][11] and further addressed some problems, the research on the helicopter is still a huge challenge. Additionally, experimental conditions are also very limited for helicopter research.…”

Section: Introductionmentioning

confidence: 99%

Neural networks-based command filtering control for a table-mount experimental helicopter

Yang

2021

Journal of the Franklin Institute

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Section: Introductionmentioning

confidence: 99%

Neural networks-based command filtering control for a table-mount experimental helicopter

Yang

2021

Journal of the Franklin Institute

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“…An Adaptive Fuzzy Kalman Filter (AFKF) has been developed to compare the performance of the proposed DLNN method with the adaptive state estimation. The AFKF approach adopts the covariancematching algorithm to evaluate the degree of matching between the actual covariance of the of the innovation sequence of the filter with its theoretical value [44]. Based on the degree of matching, an adjustment of the measurement noise matrix is computed at each state estimation cycle using a Fuzzy Inference System (FIS).…”

Section: B Comparison Study Of State Estimation Techniquesmentioning

confidence: 99%

Deep-Learning-Based Neural Network Training for State Estimation Enhancement: Application to Attitude Estimation

Al-Sharman

Zweiri

Jaradat

et al. 2020

IEEE Trans. Instrum. Meas.

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Achieving precise state estimation is needed for the unmanned aerial vehicle to perform a successful flight with a high degree of stability. Nonetheless, obtaining accurate state estimation is considered challenging due to the inaccuracies associated with the measurements of the onboard commercial-offthe-shelf (COTS) Inertial Measurement Unit (IMU). The immense vibration of the vehicle's rotors makes these measurements suffer from issues like; large drifts, biases and immense unpredictable noise sequences. These issues cannot be significantly tackled using classical estimators and an accurate sensor fusion technique needs to be developed. In this paper, a deep learning framework is developed to enhance the performance of the state estimator. A deep neural network (DNN) is trained using a deep-learning-based technique to identify the associated measurement noise models and filter them out. Dropout technique is adopted for training the DNN to avoid overfitting and reduce the complexity of nets computations. Compared to the classical estimation results, the proposed deep learning technique demonstrates capabilities in identifying the measurement's noise characteristics. As an example, an enhancement in estimating the attitude states at near hover is proved using this approach. Furthermore, an actual hover flight was performed to validate the proposed estimation enhancement method.

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Modified attitude factor graph fusion method for unmanned helicopter under atmospheric disturbance

Sun

Zeng

Liu

et al. 2022

Chinese Journal of Aeronautics

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Intelligent attitude and flapping angles estimation of flybarless helicopters under near-hover conditions

Cited by 3 publications

References 49 publications

Neural networks-based command filtering control for a table-mount experimental helicopter

Neural networks-based command filtering control for a table-mount experimental helicopter

Deep-Learning-Based Neural Network Training for State Estimation Enhancement: Application to Attitude Estimation

Modified attitude factor graph fusion method for unmanned helicopter under atmospheric disturbance

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