目的 针对电动垂直起降飞行器（eVTOL）单驾模式下驾驶员因低空湍流与多旋翼复合振动引发生物动力学穿透（Biodynamic Feedthrough,BDFT）,导致操纵精度下降与上肢肌肉代偿累积的问题,提出一种将航空人因专家经验与数据驱动寻优相结合的智能座椅人机工效协同优化设计方法。方法 建立由专家先验赋权、数字孪生寻优、物理硬约束投影、人因实证验证四阶段构成的设计流程。以德尔菲法组织航空领域专家,运用层次分析法对振动舒适性、操控精度、姿态稳定性、肌肉代偿负荷、适航安全响应5项工效指标两两比较得到判断矩阵,并通过一致性检验;将归一化权重作为近端策略优化（PPO）算法多目标奖励函数的先验系数;其次在PPO动作输出层嵌入由适航几何包络定义的安全投影模块,避免训练过程中越界;接着以典型城市空中交通（UAM）振动时序驱动数字孪生系统,迭代得到座椅设计参数的优选方案;依托单轴电磁激振台架与表面肌电（sEMG）对受试者开展主客观双规验证。结果 实验数据可得,最优方案在操纵杆处的BDFT 均方根位移较被动座椅降低约83%;12名受试者的各项数据平均值均有正向改善,前臂腕屈肌sEMG均方根值降低38%（P<0.01）、Borg CR10局部疲劳评分由6.8降至2.3（P<0.01）,ISO2631-1主观振动舒适度评级由4.7改善至2.1（P<0.01）,证明了优化方法的有效性。结论 本方法以结构化方式把专家共识数据融入数据驱动优化回路,并通过物理硬约束保证了飞行安全边界,可为eVTOL驾驶座椅的多目标工效优化设计提供有益参考。
Abstract
To address the problem that pilots of electric vertical takeoff and landing (eVTOL) aircraft in single-pilot mode experience biodynamic feedthrough (BDFT) induced by low-altitude turbulence and composite multi-rotor vibration, which degrades control precision and causes cumulative upper limb muscle compensation, the work aims to propose an ergonomic collaborative optimization design method for intelligent seats integrating aeronautical human factors expert experience with data-driven optimization. A four-stage design process was established, consisting of expert prior weighting, digital twin optimization, physical hard constraint projection, and human factor empirical verification. Aviation experts were organized via the Delphi method. The Analytic Hierarchy Process (AHP) was adopted to conduct pairwise comparisons of five ergonomic indicators, namely vibration comfort, control precision, attitude stability, muscle compensatory load and airworthiness safety response, to construct judgment matrices, followed by consistency tests. The normalized weights were taken as the prior coefficients of the multi-objective reward function in the Proximal Policy Optimization (PPO) algorithm. Secondly, a safety projection module defined by airworthiness geometric envelopes was embedded in the PPO action output layer to prevent out-of-bound states during training. Subsequently, the digital twin system was driven by typical Urban Air Mobility (UAM) vibration time series to iteratively obtain optimal schemes of seat design parameters. Subjective and objective dual-standard verification was conducted on test subjects relying on a single-axis electromagnetic vibration exciter bench and surface electromyography (sEMG). Experimental data demonstrated that the root-mean-square displacement of BDFT at the control stick under the optimal scheme was reduced by approximately 83% compared with the passive seat. All average indicators of the 12 test subjects exhibited positive improvements: the RMS value of surface electromyography (sEMG) for forearm flexors decreased by 38% (P<0.01), the Borg CR10 local fatigue score dropped from 6.8 to 2.3 (P<0.01) and the ISO 2631-1 subjective vibration comfort rating improved from 4.7 to 2.1 (P<0.01). These results verified the effectiveness of the proposed optimization method. The proposed method incorporates expert consensus data into the data-driven optimization loop in a structured manner, and guarantees flight safety boundaries via physical hard constraints. It can provide valuable references for the multi-objective ergonomic optimization design of eVTOL pilot seats.
关键词
eVTOL /
智能座椅 /
生物动力学穿透 /
近端策略优化 /
表面肌电（sEMG）
Key words
eVTOL /
intelligent pilot seat /
biodynamic feedthrough (BDFT) /
proximal policy optimization (PPO) /
surface electromyography(sEMG)
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基金
陕西省重点研发计划项目（2025SF-YBXM-231）; 陕西省社会科学基金（2025J021）
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