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Wind and Payload Disturbance Rejection Control Based on Adaptive Neural Estimators: Application on Quadrotors

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  • Jesús Enrique Sierra
  • Matilde Santos

Abstract

In this work, a new intelligent control strategy based on neural networks is proposed to cope with some external disturbances that can affect quadrotor unmanned aerial vehicles (UAV) dynamics. Specifically, the variation of the system mass during logistic tasks and the influence of the wind are considered. An adaptive neuromass estimator and an adaptive neural disturbance estimator complement the action of a set of PID controllers, stabilizing the UAV and improving the system performance. The control strategy has been extensively tested with different trajectories: linear, helical, circular, and even a lemniscate one. During the experiments, the mass of the UAV is triplicated and winds of 6 and 9 in Beaufort’s scale are introduced. Simulation results show how the online learning of the estimator increases the robustness of the controller, reducing the effects of the changes in the mass and of the wind on the quadrotor.

Suggested Citation

  • Jesús Enrique Sierra & Matilde Santos, 2019. "Wind and Payload Disturbance Rejection Control Based on Adaptive Neural Estimators: Application on Quadrotors," Complexity, Hindawi, vol. 2019, pages 1-20, January.
    • Handle: RePEc:hin:complx:6460156
    DOI: 10.1155/2019/6460156
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    References listed on IDEAS

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    1. Zhen Zhou & Hongbin Wang & Zhongquan Hu, 2018. "Event-Based Time Varying Formation Control for Multiple Quadrotor UAVs with Markovian Switching Topologies," Complexity, Hindawi, vol. 2018, pages 1-15, April.
    2. Zhenglong Wu & Zhenyu Guan & Chengwei Yang & Jie Li, 2017. "Terminal Guidance Law for UAV Based on Receding Horizon Control Strategy," Complexity, Hindawi, vol. 2017, pages 1-19, May.
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    Cited by:

    1. Cheng Guo & Delin Wang, 2019. "Frequency Regulation and Coordinated Control for Complex Wind Power Systems," Complexity, Hindawi, vol. 2019, pages 1-12, May.

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