This work develops a model predictive control (MPC) scheme using online learning of recurrent neural network (RNN) models for nonlinear systems switched between multiple operating regions following a prescribed switching schedule. Specifically, an RNN model is initially developed offline to model process dynamics using the historical operational data collected in a small region around a certain steady-state. After the system is switched to another operating region under a Lyapunov-based MPC with suitable constraints to ensure satisfaction of the prescribed switching schedule policy, RNN models are updated using real-time process data to improve closed-loop performance. A generalization error bound is derived for the updated RNN models using the notion of regret, and closed-loop stability results are established for the switched nonlinear system under RNN-based MPC. Finally, a chemical process example with the operation schedule that requires switching between two steady-states is used to demonstrate the effectiveness of the proposed RNN-MPC scheme.

中文翻译：

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具有预定模式转换的非线性系统的在线机器学习建模和预测控制

这项工作开发了一种模型预测控制 (MPC) 方案，该方案使用循环神经网络 (RNN) 模型的在线学习，用于按照规定的切换时间表在多个操作区域之间切换的非线性系统。具体来说，RNN 模型最初是离线开发的，以使用在特定稳态附近的小区域中收集的历史操作数据对过程动态进行建模。在系统切换到基于 Lyapunov 的 MPC 下的另一个运行区域后，具有适当的约束以确保满足规定的切换调度策略，RNN 模型使用实时过程数据进行更新以提高闭环性能。使用后悔的概念为更新的 RNN 模型导出泛化误差界限，在基于 RNN 的 MPC 下，建立了切换非线性系统的闭环稳定性结果。最后，使用需要在两个稳态之间切换的操作计划的化学过程示例来证明所提出的 RNN-MPC 方案的有效性。