基于非线性系统的安全传输-强化-学习的最佳控制
IEEE transactions on cybernetics
|November 4, 2024
概括
本研究引入了一个安全的转移强化学习 (TRL) 框架,以有效地优化非线性过程. 通过利用先前的知识并确保控制不变量集中的安全性,TRL显著减少了培训时间和计算成本.
科学领域:
- 过程控制 过程控制
- 人工智能的人工智能是人工智能.
- 化学工程是化学工程的组成部分.
背景情况:
- 传统的强化学习 (RL) 对于非线性过程的最佳控制在培训过程中受到高计算需求和安全问题的影响.
- 确保闭环系统的安全性至关重要,但在现有的RL方法中具有挑战性.
研究的目的:
- 提出一个安全的转移强化学习 (TRL) 框架,以加速学习和提高安全性,以最佳控制非线性过程.
- 减少计算资源需求和培训时间,以优化控制政策.
主要方法:
- 开发了一个TRL算法,利用从预训练的源任务中获得的知识,以更快地学习新的目标任务.
- 实施一个控制不变量集 (CIS) 来保证数据收集和政策优化过程中的安全.
- 提供了政策错误的理论分析,考虑了源头和目标任务的差异.
主要成果:
- 与传统的RL相比,TRL框架显著减少了学习时间和计算资源.
- 通过CIS,在整个学习过程中保证了闭环系统的安全性.
- 在化学过程案例研究中证明有效性,实现高效的最佳控制,保证安全.
结论:
- 拟议的安全TRL框架为非线性过程的计算效率高和安全的最佳控制提供了有效的解决方案.
- 利用先前的知识和在CIS中保持安全是克服传统RL局限性的关键.
- 该方法对现实世界的应用具有前景,特别是在化学过程等复杂系统中.
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