Iterative learning model predictive control (ILMPC) has been recognized as an effective approach to realize high-precision tracking for batch processes with repetitive nature because of its excellent learning ability and closed-loop stability property. However, as a model-based strategy, ILMPC suffers from the unavailability of accurate first principal model in many complex nonlinear batch systems. On account of the abundant process data, nonlinear dynamics of batch systems can be identified precisely along the trials by neural network (NN), making it enforceable to design a data-driven ILMPC. In this article, by using a control-affine feedforward neural network (CAFNN), the features in the process data of the former batch are extracted to form a nonlinear affine model for the controller design in the current batch. Based on the CAFNN model, the ILMPC is formulated in a tube framework to attenuate the influence of modeling errors and track the reference trajectory with sustained accuracy. Due to the control-affine structure, the gradients of the objective function can be analytically computed offline, so as to improve the online computational efficiency and optimization feasibility of the tube ILMPC. The robust stability and the convergence of the data-driven ILMPC system are analyzed theoretically. The simulation on a typical batch reactor verifies the effectiveness of the proposed control method.

中文翻译：

---

基于迭代数据驱动建模的迭代学习模型预测控制

迭代学习模型预测控制（ILMPC）由于其优异的学习能力和闭环稳定性，被认为是实现具有重复性的批处理过程高精度跟踪的有效方法。然而，作为基于模型的策略，ILMPC 在许多复杂的非线性批处理系统中存在无法获得准确的第一主模型的问题。由于过程数据丰富，批处理系统的非线性动力学可以通过神经网络 (NN) 在试验过程中精确识别，从而可以设计数据驱动的 ILMPC。在本文中，通过使用控制仿射前馈神经网络（CAFNN），提取前批次过程数据中的特征，形成当前批次控制器设计的非线性仿射模型。基于 CAFNN 模型，ILMPC 在管框架中制定，以减弱建模误差的影响并以持续的精度跟踪参考轨迹。由于控制仿射结构，可以离线解析计算目标函数的梯度，从而提高管ILMPC的在线计算效率和优化可行性。从理论上分析了数据驱动的ILMPC系统的鲁棒稳定性和收敛性。对典型间歇式反应器的模拟验证了所提出的控制方法的有效性。从而提高管子ILMPC的在线计算效率和优化可行性。从理论上分析了数据驱动的ILMPC系统的鲁棒稳定性和收敛性。对典型间歇式反应器的模拟验证了所提出的控制方法的有效性。从而提高管子ILMPC的在线计算效率和优化可行性。从理论上分析了数据驱动的ILMPC系统的鲁棒稳定性和收敛性。对典型间歇式反应器的模拟验证了所提出的控制方法的有效性。