Explicit Model Predictive Control for Large-Scale Systems via Model Reduction
Explicit Model Predictive Control for Large-Scale Systems via Model Reduction
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DOI：
10.2514/1.33079
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发表时间：
2008-07
影响因子：
2.6
通讯作者：
Svein Hovland;J. Gravdahl;K. Willcox
中科院分区：
文献类型：
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作者：
Svein Hovland;J. Gravdahl;K. Willcox
在本文中，我们提出了一个框架，用于实现具有快速动态的大型系统的约束最优实时控制。该方法在输出反馈实现中使用模型预测控制问题的显式解决方案与模型简化相结合。模型预测控制问题的显式求解带来了在线模型预测控制功能，而无需在每个时间步求解优化问题。降阶模型是使用面向目标、模型约束的优化公式导出的，该公式可以生成适合当前控制应用的高效模型。该方法针对一个具有挑战性的大规模流动问题进行了说明，该问题旨在控制超音速扩压器中的激波位置。
In this paper, we present a framework for achieving constrained optimal real-time control for large-scale systems with fast dynamics. The methodology uses the explicit solution of the model predictive control problem combined with model reduction, in an output-feedback implementation. The explicit solution of the model predictive control problem leads to online model predictive control functionality without having to solve an optimization problem at each time step. Reduced-order models are derived using a goal-oriented, model-constrained optimization formulation that yields efficient models tailored to the control application at hand. The approach is illustrated on a challenging large-scale flow problem that aims to control the shock position in a supersonic diffuser.