This work explores the design of distributed model predictive control (DMPC) systems for nonlinear processes using machine learning models to predict nonlinear dynamic behavior. Specifically, sequential and iterative DMPC systems are designed and analyzed with respect to closed‐loop stability and performance properties. Extensive open‐loop data within a desired operating region are used to develop long short‐term memory (LSTM) recurrent neural network models with a sufficiently small modeling error from the actual nonlinear process model. Subsequently, these LSTM models are utilized in Lyapunov‐based DMPC to achieve efficient real‐time computation time while ensuring closed‐loop state boundedness and convergence to the origin. Using a nonlinear chemical process network example, the simulation results demonstrate the improved computational efficiency when the process is operated under sequential and iterative DMPCs while the closed‐loop performance is very close to the one of a centralized MPC system.

中文翻译：

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基于机器学习的非线性过程分布式模型预测控制

这项工作探索了使用机器学习模型预测非线性动态行为的非线性过程的分布式模型预测控制（DMPC）系统的设计。具体而言，针对闭环稳定性和性能，设计并分析了顺序和迭代DMPC系统。所需操作区域内的大量开环数据用于建立长短期记忆（LSTM）递归神经网络模型，而其模型误差与实际非线性过程模型相比足够小。随后，将这些LSTM模型用于基于Lyapunov的DMPC中，以实现高效的实时计算时间，同时确保闭环状态的有界性和对原点的收敛。以非线性化学过程网络为例，