Review on Wind Resistance for Quadrotor UAVs: Modeling and Controller Design

Shen, Jiahao; Wang, Biao; Chen, Ben M.; Bu, Ruiyu; Bao, Jie

doi:10.1142/s2301385023310015

Cited by 10 publications

(3 citation statements)

References 45 publications

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“…In [39], the methods of adaptive nonlinear control techniques for quadrotors, including traditional nonlinear control methods and their adaptive/observer-enhanced variants, were summarized. In [40], the methods of controller design for quadrotors when subjected to external uncertainties such as wind were summarized, which focus on describing the wind field model, the dynamic model of the quadrotor under wind, and various control methods used for wind resistance. However, disturbance rejection techniques are not exhaustively described in [40].…”

Section: Introductionmentioning

confidence: 99%

“…In [40], the methods of controller design for quadrotors when subjected to external uncertainties such as wind were summarized, which focus on describing the wind field model, the dynamic model of the quadrotor under wind, and various control methods used for wind resistance. However, disturbance rejection techniques are not exhaustively described in [40]. In summary, most of the works focus on reviewing the control methods for quadrotors but lack a systematic review and analysis of disturbance estimation methods for quadrotors.…”

Section: Introductionmentioning

confidence: 99%

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Disturbance Observer and Adaptive Control for Disturbance Rejection of Quadrotor: A Survey

Wang,

Shen

2024

Actuators

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Quadrotors are widely applied in many fields, but they often face various external disturbances in actual operation. This makes it necessary to design a controller that can handle disturbances. Disturbance observer and adaptive control techniques are commonly used disturbance rejection techniques, the core idea of which is to estimate the disturbances in real time and incorporate the estimated values into the controller to suppress the disturbances. In this paper, various disturbance observers and adaptive control techniques, including nonlinear disturbance observers, extended state observers, neural networks, and fuzzy logic systems, are introduced, along with their variants or different structures. These techniques improve the adaptability and robustness of quadrotors to complex environments. Finally, future research directions for the disturbance rejection of quadrotors are also presented.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Disturbance Observer and Adaptive Control for Disturbance Rejection of Quadrotor: A Survey

Wang,

Shen

2024

Actuators

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“…It is widely used in various tasks such as cargo transportation, aerial photography, agricultural plant protection, rescue and relief operations, remote-sensing mapping, and reconnaissance [1][2][3][4]. A wide range of application scenarios also impose strict requirements on its flight control capability, particularly the attitude control during UAV flight [4][5][6]. However, the lightweight fuselage of a quadrotor leads to its poor ability to resist external disturbances, which reduces the accuracy of its attitude control.…”

Section: Introductionmentioning

confidence: 99%

Deep Deterministic Policy Gradient (DDPG) Agent-Based Sliding Mode Control for Quadrotor Attitudes

Hu,

Yang,

Liu

2024

Drones

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A novel reinforcement deep learning deterministic policy gradient agent-based sliding mode control (DDPG-SMC) approach is proposed to suppress the chattering phenomenon in attitude control for quadrotors, in the presence of external disturbances. First, the attitude dynamics model of the quadrotor under study is derived, and the attitude control problem is described using formulas. Second, a sliding mode controller, including its sliding mode surface and reaching law, is chosen for the nonlinear dynamic system. The stability of the designed SMC system is validated through the Lyapunov stability theorem. Third, a reinforcement learning (RL) agent based on deep deterministic policy gradient (DDPG) is trained to adaptively adjust the switching control gain. During the training process, the input signals for the agent are the actual and desired attitude angles, while the output action is the time-varying control gain. Finally, the trained agent mentioned above is utilized in the SMC as a parameter regulator to facilitate the adaptive adjustment of the switching control gain associated with the reaching law. The simulation results validate the robustness and effectiveness of the proposed DDPG-SMC method.

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Adaptive backstepping and sliding mode control of a quadrotor

Maaruf,

Abubakar,

Gulzar

2024

J Braz. Soc. Mech. Sci. Eng.

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This article proposes a simple robust adaptive control architecture with minimum parameter estimation laws for trajectory tracking of a quadrotor subjected to parametric variations and environmental disturbances. This simple control architecture aims to achieve highly accurate, robust, and fast trajectory tracking of the quadrotor in a short time with low computational cost. Firstly, the quadrotor model is divided into altitude, attitude, and position subsystems for which appropriate control methods are designed without prior knowledge of the upper bound of external disturbances. A simple adaptive fractional-order sliding mode control (AFSMC) is designed to enhance the tracking of the altitude subsystem and estimate the upper bound of the disturbances. Then, a simple adaptive backstepping control (ABC) is developed for the horizontal position to generate the required roll and pitch orientations. The adaptation laws not only estimate the upper bound of the disturbances but also adjust the controller gains thereby enhancing the robustness of the ABC. A nonsingular fast terminal sliding mode control (NFTSMC) is incorporated with a finite-time disturbance observer (FDO) to accurately suppress the disturbances, and follow the target rotation angles within a short finite-time. Simulation results showed that the compounded control structure ensures accurate, fast, and robust tracking. The AFSMC can achieve the desired altitude with a settling time of 0.31 s and root mean square error (RMSE) of 0.0454 m. The ABC can attain the target horizontal position coordinates with a settling time of (0.47 s, 0.47 s) and RMSE of (0.0370 m, 0.0518 s). The NFTSMC-FDO can achieve the desired attitude angles with a settling time of (0.11 s, 0.13 s, 0.52 s) and RMSE of (0.0098 rad, 0.0092 rad, 0.0935 rad). Performance comparisons with existing control methods in terms of settling time and RMSE demonstrated that the proposed control architecture is superior.

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Review on Wind Resistance for Quadrotor UAVs: Modeling and Controller Design

Cited by 10 publications

References 45 publications

Disturbance Observer and Adaptive Control for Disturbance Rejection of Quadrotor: A Survey

Disturbance Observer and Adaptive Control for Disturbance Rejection of Quadrotor: A Survey

Deep Deterministic Policy Gradient (DDPG) Agent-Based Sliding Mode Control for Quadrotor Attitudes

Adaptive backstepping and sliding mode control of a quadrotor

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