基于Q-学习的多变量非线性模型预测控制器:对温度轨迹跟踪的批量反应器进行实验验证
Abhiram Varma Vegesna1, Muralikrishna Shamaiah Narayanarao1, Kishore Bhamidipati1
1Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576 104, India.
ACS omega
|July 14, 2025
概括
本研究介绍了一种基于Q学习的非线性模型预测控制 (QL-NMPC) 用于批量反应堆温度控制. 强化学习方法可以实现无模型优化,以在非线性过程中有效跟踪温度.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 控制系统 控制系统
- 人工智能的人工智能
背景情况:
- 批量反应堆需要精确的温度控制以获得最佳性能.
- 传统的控制方法通常依赖于准确的系统模型,这对于非线性过程来说可能是具有挑战性的.
研究的目的:
- 引入一种基于Q学习的非线性模型预测控制 (QL-NMPC) 框架.
- 通过强化学习在批量反应堆中实现无模型温度控制.
主要方法:
- 一个强化学习代理在模拟中接受了训练,以学习最佳控制策略.
- 使用价值代的Q学习算法用于无模型的政策优化.
- 学习的控制策略是实时在使用NVIDIA Jetson Orin平台的物理反应堆上实现的.
主要成果:
- QL-NMPC框架证明了批量反应堆中有效的温度跟踪.
- 无模型方法成功地优化了控制策略,没有明确的政策评估.
- 在NVIDIA Jetson Orin平台上的实时实现验证了框架的实际适用性.
结论:
- 强化学习提供了一种强大的方法来控制非线性批处理过程.
- QL-NMPC框架为批量反应堆的温度控制提供了一个有效的,无模型的解决方案.
- 这项研究强调了人工智能的潜力,在不需要系统识别的情况下推进过程控制.
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