Design of anti-load perturbation flight trajectory stability controller for agricultural UAV

Wang, Xu; XianYu, Wu; Liu, Jiazhen; Yasheng, Yan

doi:10.3389/fpls.2023.1030203

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…The flight behavior of a quadrotor is impacted by several factors such as the wind, the weight variation, the speed, the trajectory to follow and the evolution of its own internal parameters (e.g., Wei et al, 2023 ). Due to sophisticated nonlinearity and strong coupling parameters under-actuating an absolutely unstable dynamic model, these phenomena are augmented.…”

Section: Introductionmentioning

confidence: 99%

On designing a configurable UAV autopilot for unmanned quadrotors

Bhar,

Sayadi

2024

Front. Neurorobot.

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Unmanned Aerial Vehicles (UAVs) and quadrotors are being used in an increasing number of applications. The detection and management of forest fires is continually improved by the incorporation of new economical technologies in order to prevent ecological degradation and disasters. Using an inner-outer loop design, this paper discusses an attitude and altitude controller for a quadrotor. As a highly nonlinear system, quadrotor dynamics can be simplified by assuming several assumptions. Quadrotor autopilot is developed using nonlinear feedback linearization technique, LQR, SMC, PD, and PID controllers. Often, these approaches are used to improve control and to reject disturbances. PD-PID controllers are also deployed in the tracking and surveillance of smoke or fire by intelligent algorithms. In this paper, the efficiency using a combined PD-PID controllers with adjustable parameters have been studied. The performance was assessed by simulation using matlab Simulink. The computational study conducted to assess the proposed approach showed that the PD-PID combination presented in this paper yields promising outcomes.

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Section: Introductionmentioning

confidence: 99%

On designing a configurable UAV autopilot for unmanned quadrotors

Bhar,

Sayadi

2024

Front. Neurorobot.

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Disturbance rejection control of the agricultural quadrotor based on adaptive neural network

Le,

Xie,

Chen

2024

Information Processing in Agriculture

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A Supervised Reinforcement Learning Algorithm for Controlling Drone Hovering

Wu,

Yang,

Zhuo

et al. 2024

Drones

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The application of drones carrying different devices for aerial hovering operations is becoming increasingly widespread, but currently there is very little research relying on reinforcement learning methods for hovering control, and it has not been implemented on physical machines. Drone’s behavior space regarding hover control is continuous and large-scale, making it difficult for basic algorithms and value-based reinforcement learning (RL) algorithms to have good results. In response to this issue, this article applies a watcher-actor-critic (WAC) algorithm to the drone’s hover control, which can quickly lock the exploration direction and achieve high robustness of the drone’s hover control while improving learning efficiency and reducing learning costs. This article first utilizes the actor-critic algorithm based on behavioral value Q (QAC) and the deep deterministic policy gradient algorithm (DDPG) for drone hover control learning. Subsequently, an actor-critic algorithm with an added watcher is proposed, in which the watcher uses a PID controller with parameters provided by a neural network as the dynamic monitor, transforming the learning process into supervised learning. Finally, this article uses a classic reinforcement learning environment library, Gym, and a current mainstream reinforcement learning framework, PARL, for simulation, and deploys the algorithm to a practical environment. A multi-sensor fusion strategy-based autonomous localization method for unmanned aerial vehicles is used for practical exercises. The simulation and experimental results show that the training episodes of WAC are reduced by 20% compared to the DDPG and 55% compared to the QAC, and the proposed algorithm has a higher learning efficiency, faster convergence speed, and smoother hovering effect compared to the QAC and DDPG.

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Design of anti-load perturbation flight trajectory stability controller for agricultural UAV

Cited by 4 publications

References 16 publications

On designing a configurable UAV autopilot for unmanned quadrotors

On designing a configurable UAV autopilot for unmanned quadrotors

Disturbance rejection control of the agricultural quadrotor based on adaptive neural network

A Supervised Reinforcement Learning Algorithm for Controlling Drone Hovering

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