This work proposes a real-time model predictive control (MPC) system using control Lyapunov–barrier functions (CLBF) and recurrent neural network (RNN) models to ensure simultaneous closed-loop stability and operational safety for a general class of nonlinear systems subject to time-varying disturbances. An RNN model is first developed for the nominal system (i.e., without disturbances) and incorporated in the designs of CLBF-based MPC and of CLBF-based economic MPC (EMPC) to provide state predictions for the optimization problems of MPCs. Subsequently, to improve the closed-loop performance in terms of operational safety and stability in the presence of disturbances, online learning of RNN models is incorporated within the real-time implementation of CLBF-MPC and of CLBF-EMPC to update the RNN models using the most recent process measurement data. The proposed adaptive machine-learning-based CLBF-MPC and CLBF-EMPC schemes are evaluated using a nonlinear chemical process example.

这项工作提出了一种使用控制Lyapunov势垒函数（CLBF）和递归神经网络（RNN）模型的实时模型预测控制（MPC）系统，以确保同时受一般非线性系统影响的闭环稳定性和操作安全性。随时间变化的干扰。首先为名义系统（即无干扰）开发了RNN模型，并将其纳入基于CLBF的MPC和基于CLBF的经济MPC（EMPC）的设计中，以为MPC的优化问题提供状态预测。随后，为了在存在干扰的情况下提高操作安全性和稳定性方面的闭环性能，将RNN模型的在线学习纳入了CLBF-MPC和CLBF-EMPC的实时实现中，以使用以下方法更新RNN模型最新的过程测量数据。