混合灰 Wolf-Cuckoo 搜索优化的线性二次调节器,用于强大的四旋翼控制
Renu Sharma1, Vineet Kumar2, Pallav3
1Department of Electrical Engineering, ITER, SOA University, Bhubaneshwar, Odisha, India. renusharma@soa.ac.in.
Scientific reports
|December 30, 2025
概括
这项研究使用一种新型的线性正方体调节器 (LQR) 来增强四旋翼控制,并与混合灰狼优化器-子搜索 (GWO-CS) 算法进行调整. LQR-GWO-CS控制器显著提高了无人机定位和高度控制的准确性和速度.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能和优化算法 人工智能和优化算法
- 航空航天工程 航空航天工程
背景情况:
- 四旋翼无人机 (UAV) 的精确控制对于监视和自主交付等苛刻应用至关重要.
- 现有的控制方法在动态环境中实现最佳性能和稳定性时经常面临挑战.
研究的目的:
- 开发和验证四旋翼无人机的先进控制框架.
- 使用混合优化算法来提高位置和高度控制的准确性和效率.
主要方法:
- 使用牛顿-欧勒形式主义开发了四旋翼机的非线性动态模型.
- 集成了线性二次调节器 (LQR) 与混合灰狼优化器-子搜索 (GWO-CS) 算法,以实现最佳的LQR增益调整.
- 在模拟环境中通过硬件在循环 (HIL) 测试实现并测试了LQR-GWO-CS控制器.
主要成果:
- 与传统的LQR相比,LQR-GWO-CS控制器实现了显著减少的结算时间和X和Y轴上的零超越.
- 在X轴上,整数绝对误差 (IAE) 减少了约39%,在Y轴上从1.16提高到0.70.
- 高度控制显示,降落时间从4.27秒缩短到1.96秒,且超出限度 (2.0%),优于其他测试方法.
结论:
- 拟议的LQR-GWO-CS框架为四旋翼无人机控制提供了一个强大的,高效的和数量验证的解决方案.
- 混合GWO-CS算法有效调整LQR收益,从而在位置和高度稳定方面提供卓越的性能.
- 控制器在实际无人机任务中的可行性通过模拟和在外部干扰下进行HIL测试来确认.
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