A joint guidance and control framework for autonomous obstacle avoidance in quadrotor formations under model uncertainty

Ma, Zhenwei; Wang, Qiufeng; Chen, Hongbo

doi:10.1016/j.ast.2023.108335

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…Disturbance estimation methods are widely used in various motion control scenarios such as attitude control [27,28], trajectory tracking [29][30][31], obstacle avoidance [32][33][34], and formation flying [35][36][37]. In [38], three major challenges in quadrotor controller design, including underdrivability, model uncertainty, and actuator failure, were described, and existing control methods were summarized.…”

Section: Introductionmentioning

confidence: 99%

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%

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

Wang,

Shen

2024

Actuators

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“…Radial Basis Function Neural Network (RBF) is a kind of feed-forward neural network with excellent performance. Compared with other neural networks, it has strong self-learning ability and can obtain the relationship of system control rules through online learning, so as to enhance the anti-interference ability of the system [27][28][29]. In addition, RBF neural net-work control has strong robustness and fault tolerance, and can adjust and adapt to the changes of the control system in time to meet the expected control requirements of this paper.…”

Section: Introductionmentioning

confidence: 99%

Research on Route Tracking Controller of Quadrotor UAV Based on Fuzzy Logic and RBF Neural Network

Jia,

Lin,

et al. 2023

IEEE Access

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A proportional integral differential (PID) trajectory tracking control strategy based on fuzzy logic and RBF neural network is proposed for the trajectory stability tracking control of the quadrotor unmanned aerial vehicle (UAV) control system. Firstly, the trajectory tracking problem of UAV is transformed into the command tracking control problem of PID position control loop and PID attitude control loop by transformation. Then, the fuzzy control theory is used to adjust the PID parameter gain adaptively in real time, so as to overcome the shortcomings of the traditional PID parameters relying on experience and unable to adjust in real time according to the change of the system. At the same time, the online compensator of PID parameter gain is designed by using the attention mechanism of radial basis function (RBF) neural network, and the disturbance caused by the environmental impact of the system is suppressed by online learning and adjustment. Finally, the designed controller (RFPID) is compared with the PID controller and the fuzzy-PID (FPID) controller in three simulation environments. The experimental results show that the proposed controller can significantly improve the robustness and accuracy of the trajectory tracking control of the quadrotor UAV.INDEX TERMS Unmanned aerial vehicle (UAV), trajectory tracking control, PID, fuzzy logic control, RBF neural network.

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Heterogeneous Formation Sliding Mode Control of the Flying Robot and Obstacles Avoidance

Ghaderi,

Toloei,

Ghasemi

2024

Int. J. ITS Res.

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A joint guidance and control framework for autonomous obstacle avoidance in quadrotor formations under model uncertainty

Cited by 5 publications

References 31 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

Research on Route Tracking Controller of Quadrotor UAV Based on Fuzzy Logic and RBF Neural Network

Heterogeneous Formation Sliding Mode Control of the Flying Robot and Obstacles Avoidance

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