文献类型：会议论文

英文题名：Model Predictive Control with Variable Sampling Time for Nonlinear Continuous Time Chemical Processes

作者：Xian, Shangjun[2];Wu, Tiantian[2];Li, Shenjie[2];Tian, Zhou[2];Lu, Jingyi[2]

机构：[1]Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai, Peoples R China; Qingyuan Innovat Lab, Quanzhou 362801, Fujian, Peoples R China;[2]East China Univ Sci & Technol, Shanghai 200237, Peoples R China

会议论文集：14th Asian Control Conference (ASCC)

会议日期：JUL 05-08, 2024

会议地点：Dalian, PEOPLES R CHINA

语种：英文

外文关键词：Model Predictive Control; Runge-Kutta method; variable sampling time; process control

摘要：Chemical processes often involve nonlinear continuous-time ordinary or partial differential equations. When implementing advanced control strategies like Model Predictive Control, it is necessary to discretize the continuous-time system to accommodate digital control systems. Typically, a fixed sampling time is used, but this can lead to high computational costs and subpar control performance when the system exhibits significant nonlinearity. In this paper, we propose a model predictive control scheme with time varying sampling time. Specifically, we discretize the continuous-time ordinary differential equation using both low and high order models based on the Runge-Kutta method. The sampling time is then treated as a decision variable in the MPC design. To ensure accurate predictions from the discrete-time model, an additional constraint is introduced to regulate the differences between the predictions from the two sets of models. This approach effectively balances computational costs and control performance. Numerical experiments were conducted on the control of a batch reactor to validate the efficiency of the proposed method.