Multimodel Self-Learning Predictive Control Method With Industrial Application

In industrial sites, system operation conditions fluctuate due to changes in raw material and equipment status, making it critical to identify the operation conditions and obtain appropriate controllers accurately. Additionally, even for a specific operation condition, fixed control strategies may result in mismatches due to varying operational stages. To address the accurate control of industrial processes across multiple operation conditions, this article proposes a multimodel self-learning predictive control (MSLPC) method to simultaneously improve the accuracy of offline condition partition and online control performance. Specifically, in the offline stage, for complex and multidimensional industrial data, condition indicators are selected based on expert systems and data analytics, and a “presetting precise-fusion” two-stage operation condition learning (TSOCL) algorithm is proposed to accurately identify the operation conditions of the system. In the online stage, a self-learning predictive control algorithm is proposed, which improves adaptability and control performance by adjusting controllers. This maintains a high match between the control strategy and system state. Simulation experiments demonstrate that the MSLPC method achieves higher control accuracy and faster control rate in the presence of varying operation conditions. Finally, the proposed method is deployed in a real industrial roaster to validate its effectiveness and excellent control performance.