Trajectory Tracking of a Quadcopter with an Attached Payload Mass under External Disturbances

Geronel, Renan S.; Bueno, Douglas Domingues

doi:10.1109/induscon51756.2021.9529651

Cited by 4 publications

(7 citation statements)

References 8 publications

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“…The optimizer is Adam, and the metrics are the Root Mean Square Error (RMSE). The default parameters of LSTM are shown in the table (2).…”

Section: Training the Lstmmentioning

confidence: 99%

“…Recently, in the pandemic period, the usage of UAVs as a delivery system has become very popular. Due to their low cost in installation and traffic, and their contactless delivery, drones may reach areas humans can not reach [2]. As with other technologies, drones may have bad usage such as illegal imaging for people in restricted areas and the transfer of illegal materials.…”

Section: Introductionmentioning

confidence: 99%

“…This article [2] aimed to track the trajectory of a Quadcopter with a payload under different noise scenarios by different controllers. First-order Sliding Mode Controller (FO-SMC), second-order SMC (SO-SMC), and Proportional Integral Derivative (PID).…”

Section: Introductionmentioning

confidence: 99%

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Payload Prediction for Quadro Drone using Temporal Deep Machine Learning Models

Zitar,

Kashkash,

Seghrouchni

et al. 2024

Preprint

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In this paper, four different deep-learning and temporal machine-learning techniques are used to predict the payload of the DJI Matrice 100 quadcopter drone based on its tracking data. Tracking variables for real-life experimentations are provided as open source for payloads of 0.0, 250, 500, and 750 grams. The drone is a Quado drone DJI matrice 100. The Machine Learning techniques are RNN, LSTM, TCN, and GRU. The values of the tracks’ kinematics come from several different flights for the different loads. The tracks’ kinematics and flight parameters are used in training the four models. The temporal nature of the data values triggers the need for machine learning methods that use history/memory as part of inputs. This application is a real and relevant test to evaluate the capabilities of the 4 famous types of deep temporal machine learning models. The original data has more than 1400 records for every flight with more than 22 variable values. More than 270 flights were conducted with the 4 payloads. The models were tuned and initialized with the optimum parameters found. Several simulations were implemented for training, testing, and validation. Cross-validation for the models was implemented The RMSE and MAE errors were used to evaluate the loss and the accuracies. The TCN model with the optimum parameters showed the best performance compared to other techniques. The RNN (many-to-one) showed the lowest performance. The results emphasize the superiority of the modern TCN methods over other temporal deep techniques for this kind of problem.

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“…The optimizer is Adam, and the metrics are the Root Mean Square Error (RMSE). The default parameters of LSTM are shown in the table (2).…”

Section: Training the Lstmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Payload Prediction for Quadro Drone using Temporal Deep Machine Learning Models

Zitar,

Kashkash,

Seghrouchni

et al. 2024

Preprint

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“…which guarantees the stability condition for the altitude state (z). These steps can also be extended to the position and attitude control, as seen in [10].…”

Section: Stability Analysismentioning

confidence: 99%

“…The most common UAV configuration found in industrial applications is the quadrotor design, a four-propeller aircraft design. In the presence of attached loads such as sensors or compartments to carry medical goods and organs, the quadrotor dynamics can be significantly altered, creating unwanted vibration in the system [10]. This undesired vibration can negatively affect the quadrotor aircraft's performance, as well as its payload integrity and sensing accuracy [11,12].…”

Section: Introductionmentioning

confidence: 99%

On the Effect of Flexibility on the Dynamics of a Suspended Payload Carried by a Quadrotor

Geronel

Botez

Bueno

2022

Designs

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Unmanned aerial vehicles (UAVs) have been gaining increased importance due to their variety of applications. In some specific tasks, UAVs require the addition of payloads and onboard components, including sensors, which require great stability to provide safe and reliable responses (related to the payload characteristics, such as the temperature, pressure, vibrations and many other factors). In contrast with the suspended payloads carried by a quadrotor aircraft with a rigid attachment, an elastic attachment is designed to assess the influence of the vibration characteristics on the quadrotor and its payload. Since the payload dynamics can alter the flight performance, sensor measurement accuracy and payload integrity, an adapted sliding mode control is used to guide the quadrotor on its desired trajectory and to compensate for the payload dynamics. To reduce the need for position sensors, a reduced-dimension observer is designed to estimate the payload trajectory, as well as the external disturbance behavior. Numerical simulations are performed to demonstrate that the flexibility influences the quadrotor’s dynamics and can create residual oscillation on its payload.

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