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Updated: Feb 13, 2026

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基于速度转换的自适应非线性扩展状态观察者策略,用于量子化姿势反跟踪低值的表面车辆,具有速度约束
Sung Jin Yoo1, Bong Seok Park2
1School of Electrical and Electronics Engineering, Chung-Ang University, 84 Heukseok-Ro, Dongjak-Gu, Seoul, 06974, South Korea.
ISA transactions
|February 11, 2026
概括
这项研究引入了一种新的控制策略,用于使用量化姿势反的不确定的低值表面车辆 (USV). 该方法确保尽管传感器数据有限,系统不确定性有限,但速度约束仍然得到满足.
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
- 海洋工程 海洋工程
背景情况:
- 由于其复杂的动力学和有限的驱动,未经调节的地面车辆 (USV) 存在重大控制挑战.
- 量化测量和时间变化的速度约束使现有的USV控制策略复杂化.
- 准确估计无法测量的状态和系统不确定性对于强大的USV控制至关重要.
研究的目的:
- 为具有速度约束的不确定的USV开发基于定量姿势的跟踪控制策略.
- 设计一个可适应的非线性扩展状态观察器 (ANESO),能够处理量化反和系统不确定性.
- 为了确保满足速度约束和在存在量子化错误时强大的跟踪性能.
主要方法:
- 在USV模型中应用非线性速度转换来处理速度约束.
- 提出了一种新的自适应非线性扩展状态观测器 (ANESO),用于估计无法测量的速度和不确定性.
- 使用量化姿势反和命令过后退的方法开发了一个自适应的跟踪控制策略.
- 利亚普诺夫稳定理论被用来严格证明系统稳定性和约束满足.
主要成果:
- 拟议的ANESO有效地估计了在量化反下不可测量的速度和非线性不确定性.
- 适应性跟踪控制策略成功地弥补了量子化错误和系统不确定性.
- 开发的控制系统保证了满足时间变化的速度限制.
- 模拟研究表明,与现有方法相比,拟议方法的性能优越.
结论:
- 基于ANESO的量化输出-反跟踪控制策略对具有速度约束的不确定USV有效.
- 该方法为涉及量子化测量和系统不确定性的USV控制问题提供了强大的解决方案.
- 这项研究通过解决实际传感器和执行器的局限性,推动了海洋机器人和自动驾驶车辆控制领域的发展.
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