Control Lyapunov-Barrier functions (CLBF) have been adopted to design model predictive controllers (MPC) for input-constrained nonlinear systems to ensure closed-loop stability and process operational safety simultaneously. In this work, a CLBF-MPC using an ensemble of recurrent neural network (RNN) models is proposed with guaranteed closed-loop stability and process operational safety for two types of unsafe regions, i.e., bounded and unbounded sets, for nonlinear processes. The application of the proposed RNN-based CLBF-MPC method is demonstrated through a chemical process example.

中文翻译：

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基于机器学习建模的基于Lyapunov-Barrier函数的非线性过程预测控制

控制李雅普诺夫屏障函数（CLBF）已被用于设计输入受限非线性系统的模型预测控制器（MPC），以确保闭环稳定性和过程操作安全性。在这项工作中，提出了一种使用递归神经网络（RNN）模型集成的CLBF-MPC，它对于非线性过程的两种不安全区域（即有界和无界集合）具有保证的闭环稳定性和过程操作安全性。通过化学过程实例论证了所提出的基于RNN的CLBF-MPC方法的应用。