面向融合运行的飞行员在环建模技术综述

魏麟; 杨济睿; 李秀易; 肖越; 郑远; 李诚龙

doi:10.19818/j.cnki.1671-1637.2024.04.016

面向融合运行的飞行员在环建模技术综述

doi: 10.19818/j.cnki.1671-1637.2024.04.016

魏麟^1,,
杨济睿²,
李秀易³,
肖越⁴,
郑远⁵,
李诚龙^{4, 6, ,}

1.
中国民用航空飞行学院飞行技术学院，四川广汉 618307
2.
中国民用航空飞行学院航空电子电气学院，四川广汉 618307
3.
中国民用航空飞行学院民航监察员培训学院，四川广汉 618307
4.
北京航空航天大学电子信息工程学院，北京 100191
5.
中国民用航空飞行学院计算机学院，四川广汉 618307
6.
中国民用航空飞行学院民航飞行技术与飞行安全重点实验室，四川广汉 618307

基金项目:

国家重点研发计划 2022YFB4300902

国家自然科学基金项目 U2333214

国家自然科学基金项目 U2133209

民航局安全能力建设项目 MHAQ2024033

民航飞行技术与飞行安全重点实验室开放基金项目 FZ2022KF10

详细信息

作者简介:
魏麟(1972-)，男，四川资阳人，中国民用航空飞行学院教授，从事人工智能飞行副驾驶与通用航空电子系统研究

通讯作者:
李诚龙(1990-)，男，四川绵阳人，中国民用航空飞行学院副教授，北京航空航天大学工学博士研究生

中图分类号: V249.1
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- 被引次数: 0
出版历程
- 收稿日期: 2023-12-02
- 网络出版日期: 2024-09-26
- 刊出日期: 2024-08-28

Review on pilot-in-the-loop modeling techniques facing integrated operation

WEI Lin^1
,,
YANG Ji-rui²,
LI Xiu-yi³,
XIAO Yue⁴,
ZHENG Yuan⁵,
LI Cheng-long^{4, 6
, ,}

1.
School of Flight Technology, Civil Aviation Flight University of China, Guanghan 618307, Sichuan, China
2.
Institute of Electronic and Electrical Engineering, Civil Aviation Flight University of China, Guanghan 618307, Sichuan, China
3.
Civil Aviation Administration of China Acadamy, Civil Aviation Flight University of China, Guanghan 618307, Sichuan, China
4.
College of Electronic Information Engineering, Beihang University, Beijing 100191, China
5.
School of Computer Science, Civil Aviation Flight University of China, Guanghan 618307, Sichuan, China
6.
Key Laboratory of Civil Aviation Flight Technology and Flight Safety, Civil Aviation Flight University of China, Guanghan 618307, Sichuan, China

Funds:

National Key Research and Development Program of China 2022YFB4300902

National Natural Science Foundation of China U2333214

National Natural Science Foundation of China U2133209

Safety Capability Building Project of Civil Aviation Administration of China MHAQ2024033

Open Fund Project of Key Laboratory of Civil Aviation Flight Technology and Flight Safety FZ2022KF10

More Information

Author Bio:
WEI Lin(1972-), male, professor, weilin@cafuc.edu.cn

LI Cheng-long(1990-), male, associate professor, doctoral student, lcl@cafuc.edu.cn

摘要

摘要: 研究了面向有人机与无人机融合运行环境的飞行员模型，基于有人机舱内驾驶和无人机远程遥控驾驶2种操纵类型，介绍了飞行员建模技术的发展历程和各模型特点，通过仿真软件研究了有人机与无人机飞行员在环的响应特性，讨论了通信延迟环节对于无人机驾驶回路的影响，总结了各类飞行员模型在不同任务场景下的适用性和不足之处。研究结果表明：面向融合运行环境的飞行员建模瓶颈主要体现在有人机与无人机飞行员所处的操纵回路异构、无人机飞行员缺乏临场情景意识和无人机系统指挥与控制链路(C2链路)存在不确定性延迟等方面；有人机飞行员建模总体上是根据控制理论方法描述人体结构和飞行员的操纵特性，且人工智能方法的发展使得基于智能控制理论的飞行员模型设计方法能够更好地描述飞行员的操纵特性；无人机飞行员由于处于不同的操纵回路位置，远程遥控驾驶通过C2链路实现无人机状态感知和操纵指令上行，因此，无人机飞行员模型建模更趋向于人在环路的表达，需根据应用场景设计相匹配的飞行员模型，才可更真实地反映飞行员在特定场景下的控制与决策行为特性；面向未来的融合运行环境，构建人在环路模型应重点考虑如何描述无人机远程飞行员因临场情景意识缺乏导致的决策差异，如何更好地拟合人机系统操纵特性，以及如何构建具有高效、可靠、低延迟C2链路的飞行员模型等问题。
- 航空控制 /
- 融合运行 /
- 有人机 /
- 无人机 /
- 飞行员模型 /
- 飞行员在环 /
- 指挥与控制链路
Abstract: The pilot models facing integrated operation environments involving manned aerial vehicle (MAV) and unmanned aerial vehicle (UAV) were studied. The development history of pilot modeling technologies and the characteristics of each model were introduced according to the two types of control, including cockpit piloting of MAV and remotely controlled piloting of UAV. The pilot-in-the-loop response characteristics of MAV and UAV were analyzed by simulation software, and the influence of communication delay on the piloting loop of UAV was discussed. The applicabilities and shortcomings of various pilot models in different task scenarios were summarized. Analysis results show that the bottlenecks of pilot modeling facing integrated operation environments mainly lie in the heterogeneous control loops between MAV and UAV pilots, the lack of situational awareness of UAV pilots, and the uncertainty of delay in command and control link (C2 link) of UAV system. MAV pilot modeling generally describes the human body structure and the pilot's control characteristics according to control theory methods. Moreover, with the development of artificial intelligence, pilot model design methods based on intelligent control theory can better describe the pilot's control characteristics. Due to the different positions of UAV pilots in the control loop, remotely controlled piloting relies on the C2 link to perceive UAV status and upload control commands. Therefore, UAV pilot modeling focuses on the expression of human-in-the-loop, and the matching pilot model should be designed according to the application scenario, so as to more truly reflect the pilot's control and decision-making behavior characteristics in a specific scenario. For future integrated operation environments, the human-in-the-loop model construction should focus on how to describe decision-making differences caused by the lack of situational awareness of UAV pilots during remotely controlled piloting, how to better fit human-machine system control characteristics, and how to build an efficient and reliable pilot model with low-delay C2 link.
- aviation control /
- integrated operation /
- MAV /
- UAV /
- pilot model /
- pilot-in-the-loop /
- C2 link

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图 1 飞行员模型的基本组成

Figure 1. Basic component of pilot model

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图 2 有人机/无人机飞行员模型分类

Figure 2. Classification of MAV/UAV pilot models

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图 3 有人机/无人机协同控制系统结构

Figure 3. Structure of MAV/UAV cooperative control system

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图 4 碰撞避免、自分离以及分离保证的互操作性

Figure 4. Interoperability of CA, SS, and SA

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图 5 UAS-IN-NAS运行概念概述

Figure 5. 5 Overview of UAS-IN-NAS operation concept

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图 6 包含结构飞行员模型的人机系统结构

Figure 6. Man-machine system structure with structural pilot model

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图 7 Hess结构飞行员模型结构

Figure 7. Structure of Hess structure pilot model

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图 8 包含OCM的人机闭环系统

Figure 8. Man-machine closed-loop system with OCM

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图 9 包含模糊飞行员模型的人机系统结构

Figure 9. Man-machine system structure with fuzzy pilot model

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图 10 BP神经网络结构

Figure 10. Structure of BP neural network

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图 11 远程飞行员驾驶系统接口链路架构

Figure 11. Architecture of RPAS interface link

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图 12 层级飞行员意图模型

Figure 12. Hierarchical pilot intent model

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图 13 延迟飞行员模型架构

Figure 13. Architecture of delayed pilot model

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图 14 两架飞机在近空中遭遇的贝叶斯网络模型

Figure 14. Bayesian net model of a 2-aircraft mid-air encounter

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图 15 DPZ圆的尺寸、偏置和半径与τ_DPZ的关系^[80]

Figure 15. Dimensions, offsets and radius of DPZ circles with respect to τ_DPZ

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图 16 应用DPZ触发警报^[80]

Figure 16. Trigger alerts using DPZ

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图 17 非线性决策映射元素的特征

Figure 17. Characteristics of nonlinear decision mapping element

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图 18 飞行员和飞机的补偿控制回路

Figure 18. Compensatory control loop with pilot and aircraft

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图 19 无人机飞行员在环模型

Figure 19. UAV pilot-in-the-loop model

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图 20 有人机/无人机飞行员在环仿真试验对比

Figure 20. Comparison of MAV/UAV pilot-in-the-loop simulation experiments

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表 1 飞行员模型固有参数

Table 1. Intrinsic parameters of pilot model

参数	建议值
飞行员反应时间延迟τ_d/s	0.1~0.2
神经肌肉系统的自振频率w_n/(rad·s^-1)	10
神经肌肉系统的阻尼比ξ_n	0.707
神经肌肉系统的速率反馈系数V_n	1.0

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表 2 飞行员模型自适应参数

Table 2. Adaptive parameters of pilot model

f	K_E	V_c	T_n/s	T_c/s
0	按w_cf≈2.5 rad·s^-1估计	2.0	5.0	5.0
1	按w_cf≈2.5 rad·s^-1估计	2.0	5.0
2	按w_cf≈1.5 rad·s^-1估计	10.0	2.5	2.5

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表 3 飞机动力学模型参数

Table 3. Parameters of aircraft dynamics model

参数	数值
飞机环节的增益	5.622 4
飞机分子项的时间常数	0.918
飞机等效短周期模态的无阻尼自振频率/(rad·s^-1)	4.601 8
飞机等效短周期模态的阻尼比	0.707
飞机反应时间延迟/s	0.153 7

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表 4 McRuer飞行员模型参数

Table 4. Parameters of McRuer pilot model

参数	数值
飞行员增益	1.612 93
飞行员超前补偿时间常数	2.499 5
飞行员神经肌肉延迟补偿时间常数	2.819 35
飞行员反应时间延迟/s	0.3

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表 5 Hess结构飞行员模型参数

Table 5. Parameters of Hess structure pilot model

参数	数值
飞行员增益	2.658 37
神经肌肉系统的自振频率/(rad·s^-1)	10
神经肌肉系统的阻尼比	0.707
飞行员反应时间延迟/s	0.3
中枢神经系统的反馈结构参数	2
神经肌肉系统的速率反馈系数	1.0
中枢神经系统的速率反馈系数	2.092 46×10^-7
神经肌肉系统的反馈时间常数	10
中枢神经系统的反馈时间常数	10
飞行员反应时间延迟/s	0.15

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表 6 不同类型有人机、无人机飞行员模型总结

Table 6. Summary of different types of MAV and UAV pilot models

模型	适用	年份	特点	不足
直觉飞行员模型^[16]	有人机	20世纪50年代末	可以很好地运用到目前已经发展比较成熟的线性系统中	只能用于线性系统，无法在非线性系统中描述飞行员行为
拟线性飞行员模型^[20]	有人机	20世纪50年代末	模型结构简单，对于大多数控制任务与实际系统比较吻合	无法体现飞行员内在机理，不能体现飞行员的复杂结构
结构飞行员模型^[30]	有人机无人机	20世纪70年代末到20世纪80年代初	从整体控制效果上描述飞行员的操纵特性，分析解释了人的内在机理，充分考虑了人的各子系统间的生理特性，拟配精度更高	结构飞行员模型形式复杂，没有充分的对人体的感官系统进行描述，对应用于非线性的系统依旧存在不足之处
最优飞行员模型^[40]	有人机	20世纪60年代末到20世纪70年代初	基于现代控制理论设计的时间域模型，描述了训练有素的飞行员在充分了解任务的前提下有目的的操纵行为，且可以用来描述飞行员的多通道控制行为	根本上依旧是线性的或拟线性系统的模型，不能很好地适配于非线性的系统，对飞行员的控制有要求，会在任务要求、生理心理条件下有所限制
智能飞行员模型^[50]	有人机无人机	20世纪90年代	处理了飞行员操纵行为的不精确性和不确定性，实现了更好的人机协同，提高了任务的效率和飞行的安全性，可用于非线性系统	模糊飞行员模型中存在许多设计参数，在设计和调试过程中需要大量的时间；神经网络飞行员模型各参数的物理意义不是很明确，直接用于飞行品质的评估存在困难
意图预测模型^[65]	无人机	20世纪50年代	主要作为入侵机飞行员模型，可以作为飞机飞行员和空中交通管制员感知态势的安全工具	建模方法只能用于计算密集型、提示性、指导性制导算法或用于自主系统的决策
空战模型^[72]	有人机无人机	20世纪60年代到20世纪70年代	根据空中作战体系所设计的用于创建模拟空战的飞机飞行员训练模型	不能表明人工智能的决策与人类的决策相似性；不能处理响应的可变性
延迟模型^[76]	有人机无人机	20世纪50年代末	飞行员在操纵过程中产生的时间延迟是不可避免的，延迟模型是最常使用的比较简单的模型	只有在明确有其他来源提供所需的机动时才能使用，飞行员行为产生的延迟是会发生变化的，对于变化的时间延迟需要进行阈值判断，没有解决机动的选择问题
贝叶斯网络模型^[78]	有人机无人机	20世纪末到21世纪初	用于航空混合动力系统预测预防近空碰撞中的飞机飞行员行为	当飞机采取混合机动方式时，无法提供用于多个机动选择的框架
启发式模型^[80]	有人机无人机	20世纪50年代末	使用启发式决策规则预测飞机飞行员飞行过程中的机动	只用于解决单平面飞机之间的避撞问题
类脉冲控制模型^[83]	有人机无人机	20世纪70年代	针对飞行员的高阶系统控制试验中可观察到类似脉冲的控制行为；应用移动和等待策略用于描述大延迟系统控制过程中的飞行员行为	飞行员控制模型结构复杂，参数众多，调节困难，且部分模型只能用于高阶控制系统，对于小延迟系统的适用性还需研判

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