基于神经网络PID算法的四旋翼无人机优化控制

doi:10.16180/j.cnki.issn1007-7820.2021.10.008

Abstract

Abstract:

In this study, an attitude control algorithm based on neural network PID is proposed to solve the time-varying and nonlinear problems of the quadrotor UAVs system. The algorithm has the strong adaptive ability of PID algorithm and the strong anti-interference ability of neural network. Based on the establishment of the rigid body kinematics model and dynamics model of the quadrotor UAVs, the attitude simulation control model of the quadrotor UAV MATLAB-Simulink is built, and the control effect of the neural network PID control algorithm, traditional PID and cascade PID control algorithm on the three attitude angles of the UAVs is compared. The results show that compared with the other two control algorithms, the transition time of neural network PID control algorithm is reduced from 4 s to 1 s, and the static error of the system after output stabilization is only 0.5%, indicating that the neural network PID control algorithm has high control precision and better static and dynamic characteristics.

Key words: quadrotor, unmanned aerial vehicles, dynamics modeling, simulation, traditional PID, cascade PID, neural network PID, attitude control

CLC Number:

TP273

GUO Jie,JIN Hai,SHEN Xinge. Optimization Control of Quadrotor UAVs Based on Neural Network PID Algorithm[J].Electronic Science and Technology, 2021, 34(10): 51-55.

Figures/Tables 10

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References 18

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螺旋桨直径× 螺距/m	电池电压 /V	电流/A	油门量 /%	拉力/kg	功率/W
0.127 0× 0.114 3	12.6	1.59	29	0.10	20
		4.04	50	0.20	51
		7.10	66	0.30	89
		10.20	79	0.40	129
		13.76	91	0.50	173
		18.76	100	0.63	236

参数名	符号	数值	单位
无人机总质量	m	1.5	kg
无人机轴距	L	0.25	m
杭州重力加速度	g	9.793 6	m·s^-2
绕x轴的转动惯量	I_x	5.813×10^-3	kg·m²
绕y轴的转动惯量	I_y	5.813×10^-3	kg·m²
绕z轴的转动惯量	I_z	1.108×10^-2	kg·m²
机身阻力系数	K	2.336×10^-2	N·(m·s^-1)^-2

PITCH环PID参数	P参数	I参数	D参数
经典PID	2.00	0.40	0.70
串级PID内环	1.00	0.20	0.20
串级PID外环	1.50	0.50	0.30
神经网络PID	2.05	0.30	0.55

Optimization Control of Quadrotor UAVs Based on Neural Network PID Algorithm

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