This paper addresses the problem of determining achievable set-points for artificial neural network (ANN)-based model predictive control (MPC) designs. In particular, this work considers a case where a first-principles model may not be readily available for a nonlinear process, while sufficient closed-loop data containing possibly correlated outputs is available, such that an ANN-based model that captures the nonlinear dynamics reasonably well can be identified. The paper addresses implementation aspects with such an ANN-based MPC design—specifically that of ensuring that achievable set-points are prescribed to the MPC. The key idea is to perform principal component analysis (PCA) on the training data in order to recognize existing collinearity and determine the upper confidence limit of squared prediction error (SPE) statistic. An optimization problem subject to the SPE constraint is then defined to calculate the achievable set-points, that can in turn be provided to an MPC design. The efficacy of the proposed approach is illustrated via implementations on a chemical reactor example. The results reveal the superior tracking performance of MPC using the achievable set-points over the case where arbitrarily prescribed set-points are used in the MPC implementations.

中文翻译：

---

基于人工神经网络的模型预测控制：实现可实现的设定点

本文解决了为基于人工神经网络 (ANN) 的模型预测控制 (MPC) 设计确定可实现的设定点的问题。特别是，这项工作考虑了这样一种情况，即第一性原理模型可能不容易用于非线性过程，而包含可能相关输出的足够闭环数据可用，这样基于 ANN 的模型可以合理地捕获非线性动力学好可以识别。该论文讨论了这种基于 ANN 的 MPC 设计的实现方面——特别是确保向 MPC 规定可实现的设定点。关键思想是对训练数据执行主成分分析 (PCA)，以识别存在的共线性并确定平方预测误差 (SPE) 统计量的置信上限。然后定义受 SPE 约束的优化问题来计算可实现的设定点，然后可以将其提供给 MPC 设计。通过在化学反应器示例上的实现来说明所提出方法的功效。结果表明，与在 MPC 实现中使用任意规定的设置点的情况相比，使用可实现的设置点的 MPC 具有优越的跟踪性能。