Economic multi-stage output nonlinear model predictive control
Economic multi-stage output nonlinear model predictive control
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DOI：
10.1109/cca.2014.6981580
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
S. Subramanian;S. Lucia;S. Engell
中科院分区：
文献类型：
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作者：
S. Subramanian;S. Lucia;S. Engell
非线性模型预测控制是先进控制领域最有前途的控制策略之一。它可以用来在线优化满足所有约束条件的经济成本函数，这使得它在工业应用中非常有吸引力。在过去的几年中，已经提出了几种鲁棒NMPC方法。其中，多阶段随机NMPC已被证明提供非常有前途的结果，并通过使用先进的优化工具是计算上可行的。在本文中，我们提出了一个扩展的多阶段的方法，明确考虑到不仅植物模型失配，但也通过创新采样状态估计误差。我们容纳这些错误到所产生的优化问题，包括它们在场景树制定。我们使用多模型估计算法，适合多阶段的方法。通过对一个化学反应器的模拟结果说明了该方法的有效性。
Nonlinear Model Predictive control is one of the most promising control strategies in the field of advanced control. It can be used to optimize economic cost functions online satisfying all constraints which makes it very appealing in the context of industrial applications. In the last years, several robust NMPC methods have been presented. Among them, multi-stage stochastic NMPC has been proven to provide very promising results and to be computationally feasible by the use of advanced optimization tools. In this paper, we present an extension of the multi-stage approach that takes into account explicitly not only plant-model mismatch but also state estimation error through innovation sampling. We accommodate these errors into the resulting optimization problem by including them in the scenario tree formulation. We use a multiple-model estimation algorithm that fits to the multi-stage approach. The results are illustrated by simulation results of a chemical reactor.