无人驾驶地表车辆的事件触发的自适应模糊轨迹跟踪控制,具有时间变化的输出约束和输入量化
Liangtao Jin1, Jun Ning1, Tieshan Li2
1School of Navigation, Dalian Maritime University, Dalian, 116026, China.
ISA transactions
|November 25, 2025
概括
本研究引入了无人地面车辆 (USV) 事件触发的自适应模糊控制,确保精确的轨迹跟踪,尽管输出约束和输入量化. 这种新的方法提高了控制精度,并降低了通信负载.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 海洋工程 海洋工程
- 人工智能的人工智能
背景情况:
- 无人驾驶地面车辆 (USV) 在轨迹跟踪方面面临挑战,原因是时间变化的输出约束和输入量化.
- 在USV动态中的模型不确定性需要强大的控制策略来实现可靠的性能.
- 在实际的USV控制系统中,通讯开销是一个重要的问题.
研究的目的:
- 开发一个由事件触发的适应性模糊控制策略,用于USV轨迹跟踪.
- 为应对输出约束和输入量化效应带来的挑战.
- 为了减少通信开销,同时保持高跟踪准确度.
主要方法:
- 使用模糊逻辑系统 (FLS) 来近似USV模型中的不确定性.
- 整合了一个不对称的屏障Lyapunov函数 (BLF),以确保输出约束的满足.
- 设计了一个事件触发机制和一个统一的量化器,以尽量减少通信和处理输入量化.
- 引入了输入量化线性分析模型,而不需要先前对量化器参数知识.
主要成果:
- 拟议的控制方法保证了稳定性,并实现了USV所需的跟踪精度.
- 事件触发机制通过将控制命令传输最小化,有效地减少了通信开销.
- 控制器成功地处理时间变化的输出约束和输入量化效应.
- 模拟研究验证了开发的控制策略的可行性和有效性.
结论:
- 新型事件触发的自适应模糊控制为在具有挑战性的条件下USV轨迹跟踪提供了强大的解决方案.
- BLF和模糊逻辑的集成确保了约束满足和有效的不确定性管理.
- 拟议的方法在减少网络USV系统的通信负载方面具有显著的优势.
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