Optimization strategies of Model Predictive Control (MPC) in industrial process control
工业过程控制中的模型预测控制（MPC）优化策略

摘要

模型预测控制（MPC）作为一种高效的工业过程控制方法，已广泛应用于多个领域。MPC通过优化问题求解策略，以动态系统的模型为基础，通过实时调整控制输入，优化控制性能，确保系统的稳定性和经济性。本论文探讨了工业过程控制中MPC的优化策略，特别关注如何提升其计算效率、应对系统不确定性和延迟问题。通过改进MPC的算法设计及优化计算方法，可以显著提高其在复杂工业系统中的应用效果。论文还深入分析了MPC在实际工业场景中的实现与挑战，并提出了新的优化路径。通过这一研究，旨在为工业过程控制提供更加高效、鲁棒的解决方案。

Abstract

As an efficient industrial process control method, Model Predictive Control (MPC) has been widely applied in multiple fields. Based on the model of the dynamic system, MPC solves optimization problems, adjusts control inputs in real-time, optimizes control performance, and ensures the stability and economic efficiency of the system. This thesis explores the optimization strategies of MPC in industrial process control, with a particular focus on how to improve its computational efficiency and deal with system uncertainties and delay issues. By improving the algorithm design of MPC and optimizing the calculation method, the application effect of MPC in complex industrial systems can be significantly enhanced. The thesis also conducts an in-depth analysis of the implementation and challenges of MPC in actual industrial scenarios and proposes new optimization paths. Through this research, it aims to provide more efficient and robust solutions for industrial process control.