This work focuses on the design of decentralized model predictive control (MPC) systems for nonlinear processes, where the nonlinear process is broken down into multiple, yet coupled subsystems and the dynamic behavior of each subsystem is described by a machine learning model. One decentralized MPC is designed and used to control each subsystem while accounting for the interactions between subsystems through feedback of the entire process state. The closed-loop stability of the overall nonlinear process network and the performance properties of the decentralized model predictive control system using machine-learning prediction models are analyzed. More specifically, multiple recurrent neural network models suited for each different subsystem need to be trained with a sufficiently small modeling error from their respective actual nonlinear process models to ensure closed-loop stability. These recurrent neural network models are subsequently used as the prediction model in decentralized Lyapunov-based MPCs to achieve efficient real-time computation time while ensuring closed-loop state boundedness and convergence to the origin. The simulation results of a nonlinear chemical process network example demonstrate the effective closed-loop control performance when the process is operated under the decentralized MPCs using the independently-trained recurrent neural network models, as well as the improved computational efficiency compared to the closed-loop simulation of a centralized MPC system.

这项工作的重点是针对非线性过程的分散模型预测控制（MPC）系统的设计，其中非线性过程被分解为多个但又相互耦合的子系统，并且每个机器学习模型描述了每个子系统的动态行为。设计了一个分散的MPC，用于控制每个子系统，同时通过整个过程状态的反馈考虑子系统之间的交互。分析了整个非线性过程网络的闭环稳定性，以及使用机器学习预测模型的分散模型预测控制系统的性能。进一步来说，需要使用来自各自实际非线性过程模型的足够小的建模误差来训练适用于每个不同子系统的多个循环神经网络模型，以确保闭环稳定性。这些递归神经网络模型随后在基于Lyapunov的分散式MPC中用作预测模型，以实现高效的实时计算时间，同时确保闭环状态有界和收敛到原点。非线性化学过程网络示例的仿真结果表明，使用独立训练的递归神经网络模型在分散式MPC下运行过程时，有效的闭环控制性能以及与闭环相比提高的计算效率中央MPC系统的仿真。