通过Swarm智能优化,通过事件触发的安全关键学习控制.
IEEE transactions on cybernetics
|December 19, 2025
概括
本研究介绍了一种事件触发的安全批评学习控制 (ESCLC) 算法,用于具有不对称状态约束的非线性系统. 该ESCLC算法确保了系统的安全性和稳定性,同时有效地优化控制策略.
科学领域:
- 控制理论 控制理论
- 非线性系统是非线性系统.
- 人工智能的人工智能
背景情况:
- 具有不对称状态约束的非线性系统带来了重要的控制挑战.
- 确保系统的安全性和稳定性在控制系统设计中至关重要.
- 资源有限的环境需要有效的控制策略.
研究的目的:
- 为非线性系统开发一个事件触发的安全批评学习控制 (ESCLC) 算法.
- 在不对称状态约束下保证系统安全.
- 在资源有限的场景中增强控制政策优化.
主要方法:
- 整合一个安全的批评学习控制 (SCLC) 框架与事件触发机制.
- 将控制屏障功能纳入安全值功能设计中.
- 汇聚分析和政策可接受性标准的价值代.
- 为政策改进而优化粒子群,独立于系统控制矩阵.
主要成果:
- 开发的SCLC算法保证了系统的安全性,并建立了趋同标准.
- 该ESCLC算法确保了闭环系统的非对称稳定性.
- 实际值函数的上限被导出,确保有边界的性能降低.
- 政策改进方法消除了对系统控制矩阵的依赖.
结论:
- ESCLC算法有效地解决了具有不对称状态约束的非线性系统.
- 事件触发机制提高了在资源有限的情况下的适用性.
- 对扭矩和球束系统的模拟结果验证了算法的有效性.
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