使用输出测量的时间延迟系统的规定的速率目标跟踪.
Chengye Zhang1, Ci Chen2, Frank L Lewis3
1The School of Automation, Guangdong University of Technology, Guangzhou, China; Guangdong Provincial Key Laboratory of Intelligent Systems and Optimization Integration, Guangzhou, China.
概括
本研究引入了一种新的强化学习方法,用于具有输入延迟的目标跟踪系统. 该方法通过仅使用输出测量来预测未来状态来确保精确的跟踪性能.
科学领域:
- 控制系统工程 控制系统工程
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 目标追踪系统经常因输入时间延迟而面临挑战.
- 对于许多应用程序来说,确保在延迟的情况下精确的跟踪性能至关重要.
研究的目的:
- 开发一个强化学习框架用于目标跟踪与输入时间延迟.
- 为了确保在规定的趋同率下确保跟踪性能,仅使用输出测量.
主要方法:
- 该研究将强大的输出调节理论与强化学习相结合.
- 提出了一种新的方法,即通过输入-输出测量来预测未来状态.
- 制定了政策之外的强化学习算法,以估计未来的状态并补偿延迟.
主要成果:
- 拟议的方法有效地解决了输入时间延迟的目标跟踪问题.
- 该方法将输入时间延迟和输出反跟踪统一在一个框架下.
- 数字结果验证了强化学习算法的有效性.
结论:
- 开发的算法可以在具有输入时间延迟的系统中准确地跟踪目标.
- 该方法仅依赖于输出测量,不需要准确的系统模型.
- 这项研究为现实世界跟踪应用提供了强大的解决方案.
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