Convergence of constrained model-based predictive control for batch processes
Convergence of constrained model-based predictive control for batch processes
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DOI：
10.1109/tac.2000.881002
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发表时间：
2000-10
期刊：
影响因子：
--
通讯作者：
K. Lee;Jay H. Lee
中科院分区：
文献类型：
--
作者：
K. Lee;Jay H. Lee
研究了基于约束模型的批处理预测控制 (BMPC) 的收敛特性。 BMPC是最近开发的一种控制技术，它将迭代学习控制与实时预测控制相结合。事实证明，对于一般类型的线性约束系统，随着运行次数的增加，跟踪误差收敛到零。
The convergence property of constrained model-based predictive control for batch processes (BMPC) is investigated. BMPC is a recently developed control technique that combines iterative learning control with real-time predictive control. It is proven for a general class of linear constrained systems that the tracking error converges to zero as the run number increases.