Abstract In this paper, a data-driven predictive control strategy for nonlinear system is proposed and testified on a continuous stirred tank heater (CSTH) benchmark. A recursive modified partial least square (RMPLS) algorithm is employed to regress the local linear model. The algorithm of locally weighted projection regression (LWPR) is then leveraged to build the predictive model, based on which a novel data-driven predictive control strategy is put forward. The proposed predictive controller has the ability to deal with changing working conditions, benefiting from the incremental learning ability of RMPLS and LWPR. The performance of the proposed control strategy is demonstrated with the CSTH while the superiority is illustrated by comparison with an existing model-free adaptive control approach.

中文翻译：

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应用于连续搅拌罐加热器的递归修正偏最小二乘辅助数据驱动预测控制

摘要 在本文中，提出了一种非线性系统的数据驱动预测控制策略，并在连续搅拌罐加热器 (CSTH) 基准上进行了验证。采用递归修正偏最小二乘 (RMPLS) 算法来回归局部线性模型。然后利用局部加权投影回归（LWPR）算法建立预测模型，在此基础上提出了一种新的数据驱动的预测控制策略。所提出的预测控制器具有处理不断变化的工作条件的能力，受益于 RMPLS 和 LWPR 的增量学习能力。CSTH 证明了所提出的控制策略的性能，同时通过与现有的无模型自适应控制方法进行比较来说明其优越性。