Abstract A latent variable iterative learning model predictive control (LV-ILMPC) method is presented for trajectory tracking in batch processes. Different from the iterative learning model predictive control (ILMPC) model built from the original variable space, LV-ILMPC develops a latent variable model based on dynamic partial least squares (DyPLS) to capture the dominant features of each batch. In each latent variable space, we use a state–space model to describe the dynamic characteristics of the internal model, and an LV-ILMPC controller is designed. Each LV-ILMPC controller tracks the set points of the current batch projection in the corresponding latent variable space, and the optimal control law is determined and the persistent process disturbances is rejected along both time and batch horizons. The proposed LV-ILMPC formulation is based on general LV-MPC and incorporates an iterative learning function into LV-MPC. In addition, the real physical input that drives the process can be reconstructed from the latent variable space. Therefore, this algorithm is particularly suitable for multiple-input, multiple-output (MIMO) systems with strong coupling and serious collinearity. Three studies are used to illustrate the effectiveness of the proposed LV-ILMPC .

中文翻译：

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批量过程多变量控制的潜变量迭代学习模型预测控制

摘要 针对批处理中的轨迹跟踪，提出了一种潜变量迭代学习模型预测控制（LV-ILMPC）方法。与从原始变量空间构建的迭代学习模型预测控制（ILMPC）模型不同，LV-ILMPC 开发了基于动态偏最小二乘法（DyPLS）的潜变量模型来捕捉每个批次的优势特征。在每个潜变量空间中，我们使用状态空间模型来描述内部模型的动态特性，并设计了一个 LV-ILMPC 控制器。每个 LV-ILMPC 控制器在相应的潜在变量空间中跟踪当前批次投影的设定点，并确定最优控制律，并在时间和批次范围内拒绝持续过程扰动。建议的 LV-ILMPC 公式基于通用 LV-MPC，并将迭代学习功能合并到 LV-MPC 中。此外，驱动过程的真实物理输入可以从潜在变量空间中重建。因此，该算法特别适用于耦合强、共线性严重的多输入多输出(MIMO)系统。三项研究用于说明所提议的 LV-ILMPC 的有效性。