制冷系统的最优控制分析与实用NMPC应用
Optimal control analysis and Practical NMPC applied to refrigeration systems
AI总结：
本研究针对机械压缩制冷系统，提出基于移动边界法的降阶模型，分析其可控性，并通过仿真对比实用NMPC与反馈加前馈策略，发现两者均难以达到最优循环。
摘要：
本工作聚焦于机械压缩制冷系统的最优控制。针对标准循环，提出了一种基于移动边界法的降阶状态空间模型，该模型简化了控制器设计。最优循环（即满足制冷需求同时最大化效率的循环）由三个变量定义，但仅有三个输入可用，因此研究了所提模型的可控性。通过优化仿真表明，对于一系列制冷需求，保持最小过热度并不能实现最优循环。将实用NMPC与文献中一种知名的反馈加前馈策略在仿真中进行了比较，两者在达到最优循环方面均存在困难，这与可控性研究结果一致。
英文摘要：
This work is focused on optimal control of mechanical compression refrigeration systems. A reduced-order state-space model based on the moving boundary approach is proposed for the canonical cycle, which eases the controller design. The optimal cycle (that satisfying the cooling demand while maximizing efficiency) is defined by three variables, but only two inputs are available, therefore the controllability of the proposed model is studied. It is shown through optimization simulations how optimal cycles for a range of the cooling demand turn out not to be achieved by keeping the degree of superheating to a minimum. The Practical NMPC and a well-known feedback-plus-feedforward strategy from the literature are compared in simulation, both showing trouble in reaching the optimal cycle, which agrees with the controllability study.
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