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Robust Multi-stage NMPC under Structural Plant-model Mismatch Without Full-State Measurements

机译:没有全状态测量的结构性植物模型不匹配下的稳健多级NMPC

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We address the problem of robust nonlinear model predictive control (NMPC) under plant-model mismatch in the absence of full-state measurements. We propose an approach that is based on the use of a model-error model (MEM) to handle the estimation errors and the structural plant-model mismatch in a Multi-stage NMPC framework. The MEM which consists of a linear model followed by a nonlinear operator with bounded gain captures the estimation error along with the unmodeled dynamics of the plant. Multi-stage NMPC explicitly considers the presence of feedback in the problem formulation, hence it is less conservative than other robust NMPC schemes. The advantages of the proposed scheme are demonstrated on a benchmark reactor problem.
机译:我们解决了在缺少全状态测量的情况下,工厂模型不匹配时鲁棒非线性模型预测控制(NMPC)的问题。我们提出一种基于模型误差模型(MEM)的方法来处理多级NMPC框架中的估计误差和结构性工厂模型不匹配。 MEM由线性模型和紧随其后的具有限制增益的非线性算子组成,可以捕获估计误差以及工厂的未建模动态。多阶段NMPC在问题表述中明确考虑了反馈的存在,因此它不如其他健壮的NMPC方案那么保守。提出的方案的优点在基准反应堆问题上得到了证明。

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