使用强化学习和非线性模型预测控制的自主水面船的数字双胞胎同步
Henrik Stokland Berg1, Daniel Menges1, Trym Tengesdal1
1Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway.
Scientific reports
|March 19, 2025
概括
本研究介绍了使用深度强化学习 (DRL) 和非线性模型预测控制 (NMPC) 的自主水面船 (ASV) 的自适应控制. 这提高了ASV的可靠性和在动态的海上环境中数字双胞胎同步.
科学领域:
- 海上机器人技术 在海上机器人技术
- 控制工程 控制工程 控制工程
- 人工智能的人工智能是人工智能.
背景情况:
- 目前的自主水面船 (ASV) 控制系统正在与模型不确定性和参数变化作斗争.
- 这种限制在复杂,动态的海上条件下损害了ASV的可靠性,需要适应性解决方案.
研究的目的:
- 为ASV控制开发一个集成的深度强化学习 (DRL) 和非线性模型预测控制 (NMPC) 方法.
- 确保ASV的数字双胞胎与其物理对应之间的持续同步,以提高准确性和适应性.
- 为了优化ASV控制性能,并实时识别未知的模型参数.
主要方法:
- 集成DRL以优化NMPC参数和识别未知模型参数.
- 利用数字双胞胎在模拟海上环境中为控制人员提供无风险的培训.
- 通过DRL进行实时参数识别和NMPC调.
主要成果:
- 在改善ASV控制性能方面,DRL-NMPC方法的有效性已被证明.
- 在动态和不确定的海上条件下提高ASV的可靠性和适应性.
- 数字双胞胎与物理ASVs的成功同步,通过广泛的模拟进行验证.
结论:
- 拟议的DRL-NMPC框架显著提高了ASV的安全性,效率和可靠性.
- 这种方法为动态环境中的ASV控制挑战提供了强大的解决方案.
- 该研究为先进的自主海上导航和控制系统奠定了基础.
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