标题： 混合高斯过程建模在间歇过程经济随机模型预测控制中的应用 显示英文标题

标题： Hybrid Gaussian Process Modeling Applied to Economic Stochastic Model Predictive Control of Batch Processes

摘要： 非线性模型预测控制（NMPC）是用于具有约束的非线性多变量动态系统控制的有效方法，但需要准确的工厂模型。 工厂模型通常可以从基本原理确定，但部分模型仅使用物理定律难以推导。 在本文中，提出了一种混合高斯过程（GP）基本原理建模方案来解决这一问题，该方案利用GP来对难以用基本原理描述的动态系统部分进行建模。 GP不仅提供准确的预测，还能量化该模型的剩余不确定性。 在控制算法中考虑这种不确定性至关重要，以防止约束违反和性能下降。 离线生成GP的蒙特卡洛样本，以收紧NMPC的约束，确保在线联合概率约束满足。 我们方法的优势包括快速的在线评估时间、能够考虑在线学习以减轻保守性，以及利用GP的灵活性和基本原理模型的数据效率。 该算法在一个涉及具有挑战性的半间歇生物反应器的案例研究中进行了验证。 显示英文摘要

摘要： Nonlinear model predictive control (NMPC) is an efficient approach for the control of nonlinear multivariable dynamic systems with constraints, which however requires an accurate plant model. Plant models can often be determined from first principles, parts of the model are however difficult to derive using physical laws alone. In this paper a hybrid Gaussian process (GP) first principles modeling scheme is proposed to overcome this issue, which exploits GPs to model the parts of the dynamic system that are difficult to describe using first principles. GPs not only give accurate predictions, but also quantify the residual uncertainty of this model. It is vital to account for this uncertainty in the control algorithm, to prevent constraint violations and performance deterioration. Monte Carlo samples of the GPs are generated offline to tighten constraints of the NMPC to ensure joint probabilistic constraint satisfaction online. Advantages of our method include fast online evaluation times, possibility to account for online learning alleviating conservativeness, and exploiting the flexibility of GPs and the data efficiency of first principle models. The algorithm is verified on a case study involving a challenging semi-batch bioreactor.