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Nonlinear Dynamics

, Volume 98, Issue 3, pp 1973–1998 | Cite as

Adaptive neural network finite time control for quadrotor UAV with unknown input saturation

  • Qingzheng Xu
  • Zhisheng WangEmail author
  • Ziyang Zhen
Original paper
  • 195 Downloads

Abstract

This study presents a novel adaptive robust control strategy for the position and attitude tracking of quadrotor unmanned aerial vehicles (UAVs) in the presence of input saturation, unmodeled nonlinear dynamics, and external disturbances. To deal with the negative effects of completely unknown input saturation constraints, a nonsymmetric saturation nonlinearity approxiator constructed by the hyperbolic tangent function attached with a parameter adjustment mechanism is incorporated into the controller design. Then, a novel neural network (NN) finite time backstepping-based anti-saturation control approach is proposed by introducing the NN finite time backstepping, designing the new virtual control signals and the modified error compensation mechanism. The proposed approach not only holds the advantages of the NN finite time backstepping control, but also prevents the system from degradation or even instability caused by unknown nonsymmetric saturation nonlinearities in actuator. The finite time convergence of all signals in the closed-loop aircraft system is guaranteed via Lyapunov finite time methodology despite the input saturations, unmodeled dynamics, and external disturbances. Finally, numerical simulations are carried out to illustrate the effectiveness and robustness of the proposed controller.

Keywords

Quadrotor UAVs Backstepping Neural network Finite time convergence Input saturation nonlinearity 

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China (61473144).

Compliance with ethical standards

Conflict of interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.College of Automation EngineeringNanjing University of Aeronautics and AstronauticsNanjingPeople’s Republic of China

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