基于分级PID的无人机全电刹车控制方法

doi:10.7527/S1000-6893.2025.32760

Abstract

Abstract:

To address the strong nonlinearity， high coupling， and time-varying parameters of Unmanned Aerial Vehicle （UAV） all-electric braking systems， this paper proposes a hierarchical PID control strategy. The control parameters are adjusted in different stages according to the slip ratio to improve the aircraft’s deceleration performance. First， models of aircraft ground taxiing dynamics， wheel dynamics， tire-runway friction， and all-electric actuators were developed in Simulink. Furthermore， a predictive model for the brake disc friction coefficient is established using a Genetic Algorithm （GA）-optimized BP neural network， accounting for its real-time variations with temperature and braking pressure. A hierarchical PID control system comprising baseline braking， dynamic adjustment braking， and anti-skid braking is then designed. Through simulation analysis， a comparative study was conducted on the control performance of PD+PBM control and hierarchical PID control. Experimental verification of the hierarchical PID control strategy is also carried out on a ground inertia test bench. The results demonstrate that the proposed hierarchical PID method achieves superior control performance. The error between simulation and experimental results is within 11%， validating both the correctness of the simulation model and the effectiveness of the control strategy. The hierarchical PID controller exhibits satisfactory performance， enabling the aircraft to attain high deceleration rates under various operating conditions. Even under the most extreme and adverse condition-a wet runway with wet brake disks-the average deceleration rate of the aircraft in both simulation and experiments exceeds 2 m/s²， with an error between them within 5.58%.

Key words: high-speed UAV, all-electric braking, friction coefficient prediction, hierarchical PID control, brake inertia bench

CLC Number:

V223

Guodong ZHAO, Qiaozhi YIN, Songyang ZHANG, Xiaohui WEI, Hong NIE. All-electric braking control method for UAVs based on hierarchical PID[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(11): 432760.

Figures/Tables 22

Fig.1

Fig.2

Table 1

Typical runway parameters

道面状态	$τ 1$	$τ 2$	$τ 3$
干道面	0.8	1.534 4	14.032 6
湿道面	0.4	2.019 2	8.209 8

Table 1

Fig.3

Fig.4

Fig.5

Table 2

Fig.6

Fig.7

Fig.8

Fig. 9

Table 3

Values of partial key simulation parameters

参数	数值	参数	数值
$g / (m ⋅ s - 2)$	9.8	$T S$	0.001 7
$M / k g$	3 500	$K S$	40
$V p 0 / (m ⋅ s - 1)$	78	$T e$	0.076
$R r / m$	0.2	$T m$	0.161
$J / (k g ⋅ m 2)$	0.4	$C e$	0.132
$K b d$	0.66	$C m$	1.261
$ρ / (k g ⋅ m 3)$	1.225	$c b / (N ⋅ m - 1)$	700 000
$R a / Ω$	0.5	$L s p / m$	0.004

Table 3

Fig.10

Table 4

Some key parameters during ground inertial platform testing

部分关键参数	数值
惯量轮半径/ $m$	0.7
惯量轮惯量/ $(k g ⋅ m 2)$	680
惯量轮断电时的线速度/ $(k m ⋅ h - 1)$	280
干道面机轮径向加载载荷/ $N$	14 400
湿道面机轮径向加载载荷/ $N$	7 200

Table 4

Fig.11

Fig.12

Fig.13

Fig.14

Fig.15

Fig.16

Fig.17

Table 5

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参数	数值
种群规模	20
种群最大繁衍代数	50
选择方式	轮盘赌法
交叉概率	0.4
变异概率	0.05

工况		平均刹车量	刹车距离/m	刹车时间/s	平均减速率/（m·s^-2）
干跑道干态刹车盘	仿真	141.12	739.50	18.97	4.10
	试验	137.24	741.60	19.02	4.09
	误差	2.75%	0.28%	0.26%	0.24%
干跑道湿态刹车盘	仿真	141.93	973.60	22.49	3.45
	试验	168.60	1 046.91	24.88	3.13
	误差	18.79%	7.53%	10.6%	9.28%
湿跑道干态刹车盘	仿真	80.30	1 300.00	33.21	2.34
	试验	81.51	1 355.13	34.39	2.26
	误差	1.5%	4.24%	3.55%	3.42%
湿跑道湿态刹车盘	仿真	86.71	1 519.95	36.06	2.15
	试验	111.76	1 587.21	38.40	2.03
	误差	28.89%	4.43%	6.49%	5.58%

All-electric braking control method for UAVs based on hierarchical PID

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