Journal of Engineering Research

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.

Model predictive control; Optimization strategy; Industrial process control; Computational efficiency; System uncertainty