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PPAC-Pilot:对于固定翼自动飞行员来说,规定的性能增强控制

Qiuyang Tian¹, Zelin Wang¹, Tianjiang Hu²

¹School of Aeronautics and Astronautics, Sun Yat-Sen University (Shenzhen Campus), Shenzhen 518107, China; Zhuhai Key Laboratory on Collective Intelligence and Unmanned Systems, Zhuhai 519000, China.

ISA transactions

|July 13, 2025

概括

本研究提出了一种规定的性能增强控制 (PPAC) 框架,以改进固定翼无人机 (UAV) 自动驾驶员. 通过使用历史飞行数据,PPAC增强了现有的PID控制器,确保在没有明确的无人机模型的情况下实现高度跟踪性能.

科学领域:

航空航天工程航空航天工程
控制系统控制系统
机器人技术机器人技术机器人技术

背景情况:

固定翼无人机 (UAV) 的传统自动驾驶仪往往需要大量调整才能达到最佳性能.
现有的控制系统难以保证特定的性能限制,特别是在高度跟踪方面.
历史飞行数据是控制系统开发中未充分利用的资源.

研究的目的:

为无人机自动驾驶员引入一种新的规定的性能增强控制 (PPAC) 框架.
在不需要完全重新设计的情况下,增强现有的比例整数导数 (PID) 控制循环.
利用历史飞行数据来导出动态模型和控制规律.

主要方法:

开发了一个PPAC框架来增强现有的开源自动驾驶PID控制器.
利用历史飞行数据来导出动态线性化模型和控制规律,避免了明确的无人机建模.
为了实际应用,将PPAC框架与总能控制系统 (TECS) 集成.
进行了数值模拟和Hardware-in-the-Loop (HIL) 测试以验证.

主要成果:

在起飞和巡航场景中,PPAC成功地提高了基线自动驾驶员的性能.
该框架确保了对高度跟踪错误的规定的性能限制.
对比分析表明,PPAC增强系统的性能比基线自动驾驶系统更好.
通过模拟和HIL测试的验证证实了PPAC战略的有效性.

结论:

规定的性能增强控制 (PPAC) 框架有效地增强了固定翼无人机自动驾驶.
PPAC保证了高度跟踪性能,同时最大限度地减少了对现有控制系统的重新设计工作.
利用历史飞行数据提供了一个强大的控制法推导方法,没有明确的无人机模型.

关键词:

增强控制增强控制增强控制固定翼自动飞行员的自动飞行员规定的性能规定的性能总能源控制系统总能源控制系统

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