增强型ERG控制框架：模块设计与实验验证

doi:10.7527/S1000-6893.2025.32465

Abstract

Abstract:

The control framework based on the Explicit Reference Governor （ERG） demonstrates significant application potential in the aerospace field， with its core advantage lying in achieving safety control under multiple constraints with limited computational resources. However， its practical effectiveness depends on the accuracy and conservatism of dynamic safety margin calculations. Focusing on a two-Degree-of-Freedom （2-DOF） helicopter control platform with model uncertainties， this study proposes an enhanced ERG framework integrating a lowest-order Uncertainty and Disturbance Estimator （UDE） and a risk assessment module. The framework achieves performance breakthroughs through two key designs： The introduction of the UDE to actively compensate for uncertainties， thereby improving the accuracy of trajectory prediction based on the nominal model and ensuring reliable dynamic safety margin calculation. The embedding of a risk assessment module to avoid system response delays caused by overly conservative transitions of the auxiliary reference. Multiple sets of simulation and physical comparative experiments demonstrate that the enhanced framework retains inherent advantages while solving the problems of sensitivity to model accuracy and conservative auxiliary reference adjustments， simultaneously achieving reduced steady-state error and increased response speed.

Key words: 2-DOF helicopter, uncertainty and disturbance estimator, disturbance rejection, explicit reference governor, trajectory prediction, constraints control

CLC Number:

V249

Yu JIN, Xia YANG, Lei ZHANG, Jiacheng TANG, Bo ZHU. Enhanced ERG-based control framework: Module design and experimental validation[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(9): 532465.

Figures/Tables 27

Table 1

Fig.1

Fig.2

Table 2

Simulation parameters

参数	数值	参数	数值
$J p$ /（kg·m²）	0.021 88	$T p$	0.1
$D p$ /（N·m·s·rad^-1）	0.005 72	$κ τ o$	27
$K s p$ /（N·m·rad^-1）	0.037 7	$κ θ o$	1
$θ m a x$ /rad	1	$η o$	0.01
$θ m i n$ /rad	-1	$δ θ o$	0.01
$τ p, m a x$ /（N·m）	0.1	$ζ θ o$	0.02
$τ p, m i n$ /（N·m）	-0.1	$δ τ o$	0.001
$k 1$	5	$ζ τ o$	0.002
$k 2$	3	$T$	5

Table 2

Fig.3

Fig.4

Fig.5

Table 3

Fig.6

Table 4

Parameter of helicopter platform

参数	数值
$J p$ /（kg·m²）	0.021 88
$D p$ /（N·m·s·rad^-1）	0.005 72
$K s p$ /（N·m·rad^-1）	0.037 7
$J y$ /（kg·m²）	0.022
$D y$ /（N·m·s·rad^-1）	0.022

Table 4

Table 5

Constraint parameter of helicopter platform

参数	数值	参数	数值
$θ m a x$ /rad	1	$τ p, m i n$ /（N·m）	-0.045 2
$θ m i n$ /rad	-1	$τ y, m a x$ /（N·m）	0.052 8
$τ p, m a x$ /（N·m）	0.045 2	$τ y, m i n$ /（N·m）	-0.052 8

Table 5

Table 6

Controller parameter

参数	数值	参数	数值
$k 1$	5	$ζ θ o$	0.02
$k 2$	3	$δ τ o$	3.77×10^-4
$k 3$	6	$ζ τ o$	7.54×10^-4
$k 4$	5.8	$κ θ L$	0.7
$T p$	0.25	$κ τ L$	400
$T y$	0.1	$η L$	0.01
$κ τ o$	24	$δ θ L$	0.01
$κ θ o$	1	$ζ θ L$	0.02
$T$	5	$δ τ L$	3.77×10^-4
$η o$	0.01	$ζ τ L$	7.54×10^-4
$δ θ o$	0.01

Table 6

Fig.7

Fig.8

Table 7

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

Fig.14

Table 8

Fig.15

Fig.16

Fig.17

Fig.18

Table 9

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控制方案	文献	特点
模型预测控制	［5-6，8］	最优控制性能；约束处理能力强；计算复杂度高
基于障碍李氏函数的控制	［9-10］	保证状态约束满足；处理非对称约束复杂
抗积分饱和控制	［11-13］	增强系统稳定性，有效应对输入约束；状态约束处理困难
基于参考管理器的控制	［14-16］	在线优化维度低，计算负担小；可兼容现有控制器，无需重新设计整个控制系统

评估模块	无风险评估模块	有风险评估模块
调整时间/s	5.24	3.2
辅助参考收敛时间/s	3.64	2.4

方法	基于李氏函数的ERG	本文增强型ERG
辅助参考收敛时间/s	10.68	2.4
DSM平均计算耗时/（10^-7 s）	6.466	37.77

评估模块	无风险评估模块	有风险评估模块
调整时间/s	8.72	7.80
辅助参考收敛时间/s	3.18	2.04

Enhanced ERG-based control framework: Module design and experimental validation

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