Robust Attitude Control System Design for a Distributed Propulsion Tilt-Wing UAV in Flight State Transition

Wang, Siqi; Song, Bifeng; He, Lei

doi:10.1007/978-981-13-3305-7_190

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…To assess the overall performance of each model, we introduce the "Total" value, which incorporates the normalized values of Q and F1 avg (denoted as Q norm and F1 norm , respectively). The calculation of the Total value is explained in ( 15)- (17). We would like to note that the ANN Feedback architecture is not included in our study due to its inherent instability in simulation.…”

Section: Neural Network Architecturesmentioning

confidence: 99%

“…More complex control systems have been proposed to improve the performance of UAVs under specific operation points and to enhance the stability of these vehicles in the presence of external disturbances, such as those considering the dependency between movement and attitude in our plant to offer a robust control in the presence of loads [16], un-modeled high-frequency dynamics [17], an adaptive control for multi-quadrotor cooperation under load uncertainties [18], Robust adaptive control for heterogeneous multiquadrotor cooperation [19], and Adaptive Backstepping Control to deal with uncertainties and non-linear dynamics from the UAV model [20]. However, these models often require an accurate high-order mathematical model with sophisticated coefficient computations.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning

et al. 2023

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In recent years, multi-rotor UAVs have become valuable tools in several productive fields, from entertainment to agriculture and security. However, during their flight trajectory, they sometimes do not accurately perform a specific set of tasks, and the implementation of flight controllers in these vehicles is required to achieve a successful performance. Therefore, this research describes the design of a flight position controller based on Deep Neural Networks and subsequent implementation for a multi-rotor UAV. Five promising Neural Network architectures are developed based on a thorough literature review, incorporating LSTM, 1-D convolutional, pooling, and fully-connected layers. A dataset is then constructed using the performance data of a PID flight controller, encompassing diverse trajectories with transient and steady-state information such as position, speed, acceleration, and motor output signals. The tuning of hyperparameters for each type of architecture is performed by applying the Hyperband algorithm. The best model obtained (LSTMCNN) consists of a combination of LSTM and CNN layers in one dimension. This architecture is compared with the PID flight controller in different scenarios employing evaluation metrics such as rise time, overshoot, steady-state error, and control effort. The findings reveal that our best models demonstrate the successful generalization of flight control tasks. While our best model is able to work with a wider operational range than the PID controller and offers step responses in the Y and X axis with 97% and 98% similarity, respectively, within the PID’s operational range. This outcome opens up possibilities for efficient online training of flight controllers based on Neural Networks, enabling the development of adaptable controllers tailored to specific application domains.

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Section: Neural Network Architecturesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning

et al. 2023

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“…Kasem 2019 [41] x 1 x x x x Lee 2019 [42] x 1 x x x x x Mallavalli 2019 [43] x x x 4 x x x Nigro 2019 [44] x x x x x x x x x Oukassi 2019 [45] x 3 x x x x Wang 2019a [46] x x x x Wang 2019b [47] x x 2 x x x x x x Zhang 2019 [48] x x x x x x…”

Section: Literature Analysismentioning

confidence: 99%

Review of Propulsion System Design Strategies for Unmanned Aerial Vehicles

et al. 2021

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The design of the propulsion system for Unmanned Aerial Vehicles (UAVs) demands an inclusive multidisciplinary approach from the earliest design phases, since every design choice strictly affects and is affected by the overall working conditions. This paper presents a review of the scientific literature focused on the design methods applied in defining and sizing the propulsion system of drones. The analysis, performed with a systematic approach, evaluated 123 papers according to two custom classification taxonomies, which investigated respectively the primary aim and specific content of the works. Finally, literature indications and hints were combined into an integrated framework for the functional design of the propulsion system of UAVs. The procedure aimed to support the designer in the preliminary selection of the propulsion candidates and the quick sizing of the supply system, during the first phases of the design process. According to the literature, design methods dramatically change depending on the expected applications and working conditions of UAVs, so that the detailed design of specific drone elements and propulsion components represents the focus of most of the papers in this field.

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Modeling and Robust Attitude Controller Design of a Distributed Propulsion Tilt-Wing UAV in Hovering Flight

Wang

Song

et al. 2019

2019 Chinese Control and Decision Conference (CCDC)

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Robust Attitude Control System Design for a Distributed Propulsion Tilt-Wing UAV in Flight State Transition

Cited by 4 publications

References 15 publications

Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning

Intelligent Position Controller for Unmanned Aerial Vehicles (UAV) Based on Supervised Deep Learning

Review of Propulsion System Design Strategies for Unmanned Aerial Vehicles

Modeling and Robust Attitude Controller Design of a Distributed Propulsion Tilt-Wing UAV in Hovering Flight

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