基于生物群智能优化算法的QUAV轨迹跟踪的双循环控制和状态预测分析
Zuoming Zou1, Shuming Yang1, Liang Zhao2
1Xi'an Jiaotong University, Xi'an, 710061, Shaanxi , China.
Scientific reports
|August 17, 2024
概括
这项研究介绍了四旋翼无人机 (QUAV) 强大的两层滑动模式控制系统. 先进的系统通过使用深度学习来适应干扰和预测错误来提高轨迹跟踪精度和安全性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
- 人工智能的人工智能
背景情况:
- 四旋翼无人机 (QUAV) 对于需要垂直起降 (VTOL) 的应用至关重要.
- 由于外部干扰和系统不确定性,在QUAV中保持精确的轨迹跟踪是具有挑战性的.
- 现有的控制方法经常在适应性和预测性错误缓解方面扎.
研究的目的:
- 开发一个强大的,双层控制系统,用于QUAV,以确保精确的轨迹跟踪.
- 提高QUAV系统的稳定性和适应性,以应对外部干扰和质量变化.
- 通过预测性错误缓解来提高任务操作可靠性和安全性.
主要方法:
- 为位置和态度子系统实施双层滑动模式控制策略.
- 在位置控制中使用适应机制来弥补质量和干扰.
- 集成一个深度学习模型 (LSTM与PSO) 用于轨迹错误预测和缓解.
主要成果:
- 拟议的滑动模式控制显著提高了轨迹跟踪精度.
- 适应性机制有效地弥补了系统的不确定性和外部干扰.
- 深度学习方法成功地预测和减轻跟踪错误,提高整体系统性能.
结论:
- 开发的强大的控制系统在QUAV轨迹跟踪方面表现出卓越的性能.
- 滑动模式控制和深度学习的结合为提高无人机自主性和安全性提供了有希望的方法.
- 数字模拟验证了拟议的控制策略的有效性和稳定性.
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