This paper proposes a latent variable nonlinear iterative learning model predictive control method (LV-NILMPC) based on the dynamic fuzzy partial least squares (DFPLS) model to achieve trajectory tracking and process disturbance suppression in multivariable nonlinear batch processes. The dynamic and nonlinear characteristics of the physical system are constructed by integrating the T-S fuzzy model into the regression framework of the dynamic partial least squares (PLS) inner model. The decoupling and dimensionality reduction characteristics of the DFPLS model automatically decompose a multivariable nonlinear system into multiple univariate subsystems operating independently in the latent variable space. Based on the DFPLS model, we design LV-NILMPC controllers corresponding to each latent variable subspace to track the projection of the reference trajectories. Compared with the previous control method, the method proposed in this paper has a faster convergence rate and smaller tracking error. The method is suitable for nonlinear, multivariable and strong coupling batch processes. Finally, the application of two cases shows that the method is effective.

本文提出了一种基于动态模糊偏最小二乘（DFPLS）模型的潜变量非线性迭代学习模型预测控制方法（LV-NILMPC），以实现多变量非线性批处理过程中的轨迹跟踪和过程扰动抑制。将TS模糊模型融入动态偏最小二乘（PLS）内模型的回归框架中，构建物理系统的动态和非线性特性。DFPLS 模型的解耦和降维特性自动将一个多变量非线性系统分解为多个在潜变量空间中独立运行的单变量子系统。基于 DFPLS 模型，我们设计了对应于每个潜在变量子空间的 LV-NILMPC 控制器来跟踪参考轨迹的投影。与以往的控制方法相比，本文提出的方法具有更快的收敛速度和更小的跟踪误差。该方法适用于非线性、多变量、强耦合的批处理过程。最后，两个案例的应用表明该方法是有效的。