一种基于数据的在线估计方法,用于高速飞机的空气动力学参数,考虑到强烈的非线性和噪声
Bowen Xu1, Weiqi Yang1, Yunfan Zhou1
1Advanced Propulsion Technology Laboratory, National University of Defense Technology, Changsha, China.
ISA transactions
|May 30, 2025
概括
本研究介绍了一种使用最小正方形支向量机 (LS-SVM) 和无气味卡尔曼波器 (UKF) 的在线建模方法,用于在复杂的机动中准确估计高速飞机的空气动力学参数.
科学领域:
- 航空航天工程 航空航天工程
- 控制系统 控制系统
- 计算流体动力学的流体动力学.
背景情况:
- 高速飞机的机动操作引入了复杂的空气动力学不确定性和非线性.
- 准确估计空气动力学参数对于飞行控制和稳定至关重要.
- 传统的方法与空气动力学数据的时间变化和噪音性质作斗争.
研究的目的:
- 开发一种先进的在线建模方法,以提高空气动力学参数估计的准确性.
- 为了应对高速飞行中复杂的噪音,过载,变形和非线性所带来的挑战.
- 提高空气动力学参数估计对实时数据和干扰的适应性.
主要方法:
- 设计了包含飞机动力学和动力学的增强状态变量.
- 最小方位支向量机 (LS-SVM) 用于数据驱动的参数合的在线识别.
- 无气味卡尔曼波器 (UKF) 用于在线状态变量估计和参数更新.
主要成果:
- 提出的方法有效地将时间变化的参数识别转化为在线状态变量估计.
- LS-SVM成功地模拟了空气动力学参数之间的复杂关系.
- UKF证明了对数据噪声和外部干扰的更好的适应性,提高了估计准确性.
结论:
- 结合的LS-SVM和UKF方法为在线空气动力学参数估计提供了强大的解决方案.
- 这种方法显著提高了高速飞机的空气动力学模型的准确性和适应性.
- 理论分析和模拟验证了拟议的在线建模策略的有效性.
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