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  • 1Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka 576 104, India.

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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.