概括
在分布式热过程中,频繁的模型更新和控制器激活会导致过度计算. 双事件触发空间模型预测控制 (DET-SMPC) 通过仅在必要时激活控制器来减少计算,从而提高性能.
科学领域:
- 控制工程 控制工程 控制工程
- 数据驱动系统数据驱动系统
- 热过程管理 热过程管理
背景情况:
- 分布式热处理过程经常因过度更新模型和控制器激活而遭受性能降低.
- 这些系统中的过度计算导致效率低下和控制结果不足.
研究的目的:
- 为分布式热过程开发一种新的双事件触发空间模型预测控制 (DET-SMPC) 策略.
- 为了减轻频繁的模型更新和控制器激活引起的性能问题.
- 通过数据驱动的框架来提高整体系统性能.
主要方法:
- 使用时间/空间定理构建了一个时空模型,以适应时间变化的动态.
- 建议采用自适应模型更新 (MU) 方法,使用错误触发生成器来确定最佳切换模式.
- 引入了一个由事件触发的模型预测控制 (ET-MPC) 具有由Lyapunov函数衍生的激活值.
主要成果:
- 拟议的DET-SMPC有效地减少了过度计算,仅在达到预定义的值时才能触发控制器激活.
- 适应式MU方法确定了首选的切换模式,优化了控制器的性能.
- 模拟和实验性烤箱研究验证了DET-SMPC的有效性.
结论:
- DET-SMPC提供了一个强大的解决方案,用于提高分布式热过程中的性能.
- 事件触发机制显著减少计算负载,同时保持有效的控制.
- 这种数据驱动的方法在现实世界热管理系统中证明了其实际适用性.
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