文献类型：期刊文献

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

作者：Xian, Shangjun[1]; Wu, Tiantian[1]; Li, Shenjie[1]; Tian, Zhou[1]; Lu, Jingyi[1]

机构：[1] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Qingyuan Innovation Laboratory, Quanzhou 362801, China, East China University of Science and Technology, Shanghai 200237, China

年份：2024

起止页码：1730

外文期刊名：14th Asian Control Conference, ASCC 2024

收录：EI(收录号：20244117170353)

语种：英文

外文关键词：Batch reactors - Chemical variables control - Continuous time systems - Digital control systems - Discrete time control systems - Nonlinear equations - Numerical control systems - Predictive control systems - Time varying control systems

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. ? 2024 Asian Control Association.