按电机驱动的昆虫飞翼MAV机制的自适应控制策略
Spoorthi Singh1,2, Meet Hitesh Jain1, Kanishk Kaushal3
1Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Scientific reports
|August 5, 2025
概括
本研究介绍了使用按振动机电机的昆虫规模的微型机翼飞行器 (FWMAV) 的创新设计和自适应控制. 先进的自我调节分数模糊控制 (SRFFC) 增强了稳定性和机动性,优于传统方法.
科学领域:
- 机器人和微系统工程 机器人和微系统工程
- 生物启发工程 生物启发工程
- 控制系统理论 控制系统理论
背景情况:
- 折叠翼微型飞行器 (FWMAV) 提供节能,高度机动的飞行,模仿自然飞行员.
- 昆虫规模的FWMAV面临着像按振动机电机这样的紧执行器的挑战,影响受控运动和耐用性.
- 现有的控制策略需要加强,以应对机械约束和环境干扰.
研究的目的:
- 介绍昆虫规模的FWMAV的创新设计和适应性控制策略.
- 在FWMAV应用中评估自我调节分数模糊控制 (SRFFC) 和分数PID (FPID) 的性能.
- 通过使用基于AI的观察员来增强FWMAV的稳定性,功率效率和干扰拒绝.
主要方法:
- 利用紧的按振动机电机和简化的曲折滑动机机制来启动翅膀的飞.
- 使用SIMSCAPE多体和Compmech GIM进行详细的建模和结构运动分析.
- 在模拟和现实条件下评估先进的控制策略 (SRFFC,FPID) 和基于AI的干扰观察员.
主要成果:
- 与FPID相比,SRFFC表现出更高的效率和干扰排斥能力,其表现得如上升时间,沉降时间和IAE等性能指标.
- 基于AI的干扰观察器通过补偿环境因素来提高稳定性和功率效率.
- 实验验证证了SRFFC和模块化电机配置对FWMAV机动的有效性.
结论:
- 基于SRFFC的设计和模块化电机配置显著提高FWMAV的性能,控制和适用性.
- 拟议的自适应控制策略和基于人工智能的干扰观察器为微型天线系统提供了强大的解决方案.
- 这项研究为先进的,以自然为灵感的微型飞行器铺平了道路,并提高了运营能力.
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