This paper proposes a hybrid Gaussian process (GP) approach to robust economic model predictive control under unknown future disturbances in order to reduce the conservatism of the controller. The proposed hybrid GP is a combination of two well-known methods, namely, kernel composition and nonlinear auto-regressive. A switching mechanism is employed to select one of these methods for disturbance prediction after analyzing the prediction outcomes. The hybrid GP is intended to detect not only patterns but also unexpected behaviors in the unknown disturbances by using past disturbance measurements. \textcolor{black}{A novel forgetting factor concept is also utilized in the hybrid GP, giving less weight to older measurements, in order to increase prediction accuracy based on recent disturbances values.} The detected disturbance information is used to reduce prediction uncertainty in economic model predictive controllers systematically. The simulation results show that the proposed method can improve the overall performance of an economic model predictive controller compared to other GP-based methods in cases when disturbances have discernible patterns.

中文翻译：

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鲁棒经济模型预测控制的混合高斯过程方法

本文提出了一种混合高斯过程 (GP) 方法，用于在未知的未来干扰下进行稳健的经济模型预测控制，以减少控制器的保守性。所提出的混合 GP 是两种众所周知的方法的组合，即内核组合和非线性自回归。在分析预测结果后，采用切换机制来选择这些方法中的一种进行干扰预测。混合 GP 旨在通过使用过去的干扰测量不仅检测模式，还检测未知干扰中的意外行为。\textcolor{black}{混合 GP 中还使用了一种新颖的遗忘因子概念，对较旧的测量值给予较少的权重，以便根据最近的干扰值提高预测精度。} 检测到的干扰信息用于系统地减少经济模型预测控制器中的预测不确定性。仿真结果表明，在干扰具有可辨别模式的情况下，与其他基于 GP 的方法相比，所提出的方法可以提高经济模型预测控制器的整体性能。