在等级控制框架中高层决策:集成自主系统的HMDP和MPC
IEEE transactions on cybernetics
|March 4, 2025
概括
本研究介绍了一种用于自主决策的混合框架,将连续动态与马尔科夫决策过程 (MDP) 整合在一起. 这种方法可以确保在复杂环境中对智能汽车等系统进行安全和最佳的控制.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 自主系统面临的挑战是将离散决策与连续动态整合起来.
- 不断变化的运营环境需要适应性控制策略.
- 现有的模型往往难以将高级别的离散逻辑和低级别的连续控制相结合.
研究的目的:
- 为混合系统中自主决策提出一个全面的框架.
- 开发一种新的建模方法,整合离散和连续动态.
- 为了确保自主控制设计的安全性和最佳性.
主要方法:
- 他将决策系统建成混合系统:一个受控的马尔科夫决策过程 (MDP) 与自主连续动态相结合,称为混合马尔科夫决策过程 (HMDP).
- 为HMDP开发了一个使用模型预测控制 (MPC) 的决策方案.
- 通过控制设计和分析,确保递归可行性和稳定性.
主要成果:
- 拟议的HMDP框架有效地整合了离散和连续状态变量,以实现可靠的决策.
- 模型预测控制 (MPC) 的实施保证了递归可行性和稳定性.
- 模拟演示了框架在处理复杂,动态环境中的能力,以自动变化车道为例.
结论:
- 开发的混合框架为自主决策挑战提供了强大的解决方案.
- 集成MDPs和连续动态提供了增强的安全性和最佳性.
- 该框架显示了智能运输系统及其他领域应用的重大前景.
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