An overview of the recent developments of time-series neural network modeling is presented along with its use in model predictive control (MPC). A tutorial on the construction of a neural network-based model is provided and key practical implementation issues are discussed. A nonlinear process example is introduced to demonstrate the application of different neural network-based modeling approaches and evaluate their performance in terms of closed-loop stability and prediction accuracy. Finally, the paper concludes with a brief discussion of future research directions on neural network modeling and its integration with MPC.

中文翻译：

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用于模型预测控制的神经网络建模方法的教程回顾

概述了时间序列神经网络建模的最新发展及其在模型预测控制 (MPC) 中的应用。提供了有关构建基于神经网络的模型的教程，并讨论了关键的实际实现问题。引入了一个非线性过程示例来演示不同基于神经网络的建模方法的应用，并评估它们在闭环稳定性和预测精度方面的性能。最后，本文最后简要讨论了神经网络建模及其与 MPC 集成的未来研究方向。