使用FOPID-TID控制器和HAOAROA算法控制无人机路径规划的混合优化方法的性能分析
Noorulden Basil1, Abdullah Fadhil Mohammed2, Bayan Mahdi Sabbar3
1Department of Electrical Engineering, College of Engineering, Mustansiriyah University, Baghdad, Iraq. noorulden@uomustansiriyah.edu.iq.
Scientific reports
|February 9, 2025
概括
一个新的混合优化算法,基于FOPID-TID的HAOAROA,显著改善了无人机 (UAV) 轨迹规划. 这种先进的方法可以将轨道长度减少10%,并在复杂的环境中提高稳定性.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能和优化的优化
- 航空航天工程 航空航天工程
背景情况:
- 无人驾驶飞行器 (UAV) 需要高效的轨道优化来实现复杂环境的导航.
- 像A*,JPS,Bezier和L-BSGF这样的传统算法在性能和计算效率方面存在局限性.
- 整合先进的控制策略对于提高无人机能力至关重要.
研究的目的:
- 对无人机进行比较分析各种轨迹优化算法.
- 介绍和评估基于FOPID-TID的新型混合阿基米德优化算法-骑手优化算法 (HAOAROA).
- 证明拟议方法在轨迹长度,流性,稳定性和计算效率方面的优越性.
主要方法:
- 开发基于FOPID-TID的HAOAROA,集成分数顺序控制和混合优化.
- 对传统算法进行比较模拟分析 (A*,JPS,Bezier,L-BSGF).
- 评估指标包括轨迹长度,流性,稳定性和计算时间.
主要成果:
- 与传统方法相比,基于FOPID-TID的HAOAROA实现了轨迹长度减少10%.
- 拟议的算法显示出卓越的轨迹流性和整体稳定性.
- 观察到增强的动态响应,干扰排斥和控制精度,特别是在具有挑战性的环境中.
- 基于FOPID-TID的HAOAROA被证明在计算上更高效.
结论:
- 基于FOPID-TID的HAOAROA为UAV在复杂环境中的轨迹规划提供了显著的性能改进.
- 分数顺序控制提高了动态响应和精度,超过了传统的子程序.
- 这项研究验证了混合优化的有效性,用于推进无人机控制系统.
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