论文标题
神经NARX模型学到的系统的无偏移非线性MPC方案
An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models
论文作者
论文摘要
本文介绍了非线性MPC控制器的设计，该设计提供了由神经非线性自动回归外源性（NNARX）网络描述的模型的无偏移设定值跟踪。 NNARX模型是从工厂收集的输入输出数据中标识的，并且可以通过过去的输入和输出变量为已知的可测量状态给出状态空间表示，因此不需要状态观察者。在训练阶段，与植物的行为一致时，可以强制强迫输入到国家稳定性（ΔISS）。然后，利用ΔISS属性在输出跟踪误差上采取明确的积分动作来增强模型，从而使无偏移的跟踪功能可以达到设计的控制方案。提出的控制结构在水加热系统上进行了数值测试，并将所达到的结果与另一种流行的无偏移MPC方法评分的结果进行了比较，这表明即使在植物上作用着骚动，提出的方案也具有出色的性能。
This paper deals with the design of nonlinear MPC controllers that provide offset-free setpoint tracking for models described by Neural Nonlinear AutoRegressive eXogenous (NNARX) networks. The NNARX model is identified from input-output data collected from the plant, and can be given a state-space representation with known measurable states made by past input and output variables, so that a state observer is not required. In the training phase, the Incremental Input-to-State Stability (δISS) property can be forced when consistent with the behavior of the plant. The δISS property is then leveraged to augment the model with an explicit integral action on the output tracking error, which allows to achieve offset-free tracking capabilities to the designed control scheme. The proposed control architecture is numerically tested on a water heating system and the achieved results are compared to those scored by another popular offset-free MPC method, showing that the proposed scheme attains remarkable performances even in presence of disturbances acting on the plant.
