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Adaptive Neural Dynamic Surface Attitude Control of a Quadrotor

Jianjun Ma, Peng Li, Lina Geng, Zhiqiang Zheng

Abstract


In this paper, robust adaptive flight control is de-veloped for quadrotor UAVs with external disturbances. Dynamic surface control (DSC) is designed to produce the desired moments and can avoid the explosion of complexity in traditional backstep-ping design. On-line learning radial basis function neural networks (RBFNN) are designed to handle the unknown uncertainties and
external disturbances. The stability of the design is demonstrated through Lyapunov synthesis where the closed-loop control system is proved to be semi-globally uniformly ultimately bounded (SGU-UB), and the tracking error converges to a small neighborhood of origin. Simulation results are presented to show the effectiveness of the proposed control.

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References


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DOI: http://dx.doi.org/10.21535%2FProICIUS.2014.v10.284

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