相关实验视频
Updated: Sep 16, 2025

08:12
Experimental Methods to Study Human Postural Control
Published on: September 11, 2019
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定期事件触发的强大的轨迹跟踪控制器,用于未经调整的无人驾驶地表车辆,没有速度测量
Enhua Zhang1, Jian Wang1, Xing Wang2
1Key Laboratory of Marine Intelligent Equipment and System Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China; School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
ISA transactions
|July 9, 2025
概括
本研究引入了一种用于无人地面车辆 (USV) 的新型周期性事件触发控制 (PETC) 算法,用于在有限的带宽下管理未知的干扰. 该方法提高了控制效率,并确保了系统的稳定性.
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) 面临着未知的干扰和有限的通信带宽的挑战.
- 现有的控制策略往往会孤立控制道,限制集成系统的性能.
- 在带宽限制下有效控制对于自主海洋应用至关重要.
研究的目的:
- 开发一个强大的控制算法,用于在未知的干扰和受限制的通信带宽下运行的低值USV.
- 为统一的控制器设计整合曲率和冲压控制通道.
- 提高USV控制系统的计算和通信效率.
主要方法:
- 基于输出重新定义的动态反转 (ORDI) 来重新制定USV模型并实现相对的程度.
- 最小学习参数辐射基础函数神经网络 (RBFNN) 具有适应性规律,用于近似非线性动态和扰动.
- 一个反聊天速度观察器,仅使用位置数据进行准确的速度估计.
- 一个周期性事件触发控制 (PETC) 算法平衡周期性采样和事件触发控制,使用滑动模式分流器.
主要成果:
- 拟议的ORDI方法整合了直接控制器设计的曲率和激增动态.
- RBFNN有效地接近系统动态和外部干扰,减少计算.
- 该PETC算法动态调整通信频率,优化资源使用.
- 理论分析证实了状态和估计错误的趋同,确保了系统的稳定性.
结论:
- 开发的PETC算法为未达标的USV提供了强大的和高效的控制解决方案.
- 综合控制框架和自适应学习在不确定性和带宽限制下提高性能.
- 数字模拟验证了拟议方案在具有挑战性的海洋环境中的有效性和稳定性.
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