多个观察者自适应融合用于不确定性估计及其应用于轮速系统的应用
IEEE transactions on cybernetics
|April 10, 2024
概括
本研究提出了一种用于实际不确定性估计,减少峰值和改善动态行为的新方法. 多个扩展状态观察器 (ESO) 的自适应融合增强了对噪声的稳定性,并确保了准确,快速的估计.
科学领域:
- 控制系统工程 控制系统工程
- 机器人技术 机器人技术 机器人技术
- 信号处理 信号处理
背景情况:
- 现实世界的不确定性估计面临着峰值现象和测量噪声的挑战.
- 现有的方法努力有效地减轻这些复杂性,影响动态系统性能.
- 准确的不确定性估计对于强大的控制和动态系统的可靠运行至关重要.
研究的目的:
- 引入一种用于实际不确定性估计的新方案,该方案解决了峰值动态和测量噪声.
- 提高不确定性估计框架的整体动态行为和稳定性.
- 通过理论分析,模拟和实验测试来验证拟议的方法.
主要方法:
- 开发一个核聚变估计框架,利用多个扩展状态观察员 (ESO) 进行一次性不确定性.
- 实施低频自适应参数学习技术,以实现最佳的融合近似.
- 将级联过器集成到自适应融合框架中,以改善噪声排斥.
主要成果:
- 适应性融合估计有效地减轻了不确定性估计中的过渡性峰值.
- 拟议的方法实现了快速融合和高精度,特别是在ESO的高收益调度下.
- 通过理论分析和实验验证,通过理论分析和实验验证来证明对测量噪声的增强强度.
结论:
- 新型自适应融合估计方案为不确定性估计挑战提供了实际解决方案.
- 该方法显著改善了复杂系统中的动态行为,峰值排斥和噪声强度.
- 在移动机器人轮速系统上验证,证明其可行性和有效性.
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