Journal of Guangxi Normal University(Natural Science Edition) ›› 2017, Vol. 35 ›› Issue (2): 24-31.doi: 10.16088/j.issn.1001-6600.2017.02.004

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Study of Applying BP Neural Network with Inertia Term Self-tuningto Attitude Stability of Quadrotor Unmanned Aerial Vehicle

ZHONG Haixin, QIU Senhui, LUO Xiaoshu*, TANG Tang, YANG Li, ZHAO Shuai   

  1. College of Electronic Engineering, Guangxi Normal University, Guilin Guangxi 541004, China
  • Online:2017-07-25 Published:2018-07-25

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Abstract: Due to the shortcomings that traditional PID control fails to update the tuning of Kp, Ki, Kd parameters in real time and the control accuracy is not high, this paper puts forward a kind of attitude stability control method of the combination between BP neural network with inertia term and PID control. And the inertia coefficient is modified to achieve Quadrotor Unmanned Aerial Vehicle(QUAV)’s attitude control during the flight under disturbance. The simulation experiment analysis presents that when compared with the BP neural network parameters self-tuning PID control and the traditional PID control, this control method improves the performance of anti-disturbance and robustness and the dynamic performance, thus holding practical value for reference to improve the attitude stability control of QUAV.

Key words: PID control, BP neural network, parameters self-tuning, anti-disturbance, robustness

CLC Number: 

  • TP273.2
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