针对USV的事件触发的自适应跟踪控制基于增强的优化后退技术
Hugan Zhang1, Xianku Zhang2, Yongjin Liu2
1Merchant Marine College, Shanghai Maritime University, Shanghai, 201306, China.
本研究引入了无人地面车辆的自适应控制方法,减少通信负载并改善干扰排斥. 这种新的方法提高了轨迹跟踪性能和系统对环境因素的稳定性.
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) 在轨迹跟踪方面面临挑战,原因是环境干扰,通信限制和复杂的控制设计.
- 现有的控制方法往往难以实时适应动态的海洋条件和高效的数据处理.
研究的目的:
- 开发一种针对USV的增强适应性最佳后退控制方法.
- 为了解决干扰排斥,传感器通信负担和控制器复杂性的局限性.
主要方法:
- 为了最佳的控制决策和绩效评估,采用了关键行为体强化学习框架.
- 神经网络被用来估计成本函数梯度,简化了计算.
- 实施了一个事件触发机制,以减少传感器通信频率.
- 开发了一个干扰观察器,用于实时估计和补偿环境干扰.
主要成果:
- 拟议的方法显著降低传感器通信频率.
- 环境干扰的实时补偿提高了系统的稳定性.
- 理论分析证实了控制算法的稳定性和有效性.
- 与传统方法相比,模拟结果显示出更高的性能.
结论:
- 增强的适应性最佳后退控制方法为USV轨迹跟踪提供了强大的和高效的解决方案.
- 事件触发机制和干扰观察器的集成在实际USV应用中提供了显著的优势.
- 该研究验证了在复杂的海洋控制场景中关键行为体强化学习的有效性.
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