Data-Driven Iterative Learning Temperature Control for Rubber Mixing Processes

作     者：Chi, Ronghu Zhou, Zhihao Zhang, Huimin Lin, Na Huang, Biao 

作者机构：Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Peoples R China Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai 264005 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton T6G 2G6 AB Canada 

出 版 物：《IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING》 (IEEE Trans. Autom. Sci. Eng.)

年 卷 期：2025年第22卷

页      面：10274-10286页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 

基　　金：National Science Foundation of China [62273192, 62203245] Talent Introduction and Cultivation Plan for Youth Innovation of Universities in Shandong Province 

主　　题：Temperature control Iterative methods Uncertainty Rubber Mathematical models Batch production systems Process control Hands Compounds Artificial neural networks Rubber mixing process non-identical initial states nonrepetitive uncertainties different batch lengths data-driven control iterative learning temperature control 

摘      要：Considering the four challenges of non-identical initial states, non-repetitive uncertainties, different batch lengths, and unavailable mathematical model of a rubber mixing process (RMP), this article proposes a data-driven iterative learning temperature control (DDILTC) for the RMP. Specifically, an iterative linear data model (iLDM) is developed to formulate the iterative dynamics of RMP and is further used as a one-step iterative linear predictive model to estimate the RMP s temperature that is unavailable when the current batch length is shorter than the desired one. The unknown parameters of the iLDM are estimated iteratively by designing an iterative adaption law. Further, an iterative learning based observer is designed to estimate the non-repetitive uncertainties and non-identical initial states as an extended state. The proposed DDILTC is a data-driven method and the iLDM is only used to formulate the iterative relationship of the input-output between two batches instead of a mathematical model of the RMP with physical meanings. Simulation study verifies the results. Note to Practitioners-The mixing temperature of a rubber mixing process (RMP) is a critical variable, ensuring the desired plasticity and viscosity of the rubber compounds. Indeed, RMP is a typical batch process performing repetitively over the finite time interval. However, no ILC results about the RMP temperature control have been reported even though ILC can learn the control experience from the past batches to improve control performance. The main reason lies in that the practical environments of RMP make it impossible to satisfy the strictly repetitive conditions, i.e., the initial states, disturbances, and batch lengths are all iteration-varying. Furthermore, it is difficult to establish a mathematical model of the RMP due to its large production scale and complex dynamics along both time and iteration directions. Therefore, the main motivation of this paper is to study the iterative lea