基于CPS框架的微粉生产过程多模型自适应控制

针对矿渣微粉（Ground granulated blast-furnace slag，GGBS）生产这一多变量、强耦合、多工况的复杂非线性过程，本文根据大量生产数据，提炼出矿渣微粉生产过程的三个典型工况.求解多工况多目标优化问题以求得最优设定值.建立多工况下的递归神经网数据驱动模型，并采用自适应动态规划方法，建立多个控制器，结合加权多模型控制，实现矿渣微粉生产过程在多工况切换情况下的自适应控制.通过过程运行优化、跟踪控制优化、通讯、工业以太网等信息资源与矿渣微粉生产物理资源之间的融合，构建基于信息物理系统（Cyber-physical system，CPS）的矿渣微粉生产优化控制系统.实验分析表明，本文提出的基于CPS的多模型自适应控制器，能够有效实现多工况条件下矿渣微粉生产过程的自适应控制，减小超调量，提高控制品质.

CPS-based Multiple Model Adaptive Control of GGBS Production Process

Considering the multivariable, strong-coupling, multi-conditions complex nonlinear ground granulated blast-furnace slag (GGBS) production process, this paper extracts three typical working conditions based on massive process data. Multiple optimal setpoints are obtained by resolving the multi-objective problems under different working conditions. For each condition, a data-based model is established using the recurrent neural network. Correspondingly, multiple controllers are designed by the adaptive dynamic programming method. Adopting the weighted multiple model adaptive control, adaptive control of the GGBS production in multiple conditions is realized. Integrating cyber resources including process operating optimization, tracking control optimization, communication, industrial Ethernet and physical resource of GGBS production, a optimal control system of GGBS production process is constructed based on the cyber-physical system (CPS). Experiment shows that the proposed multiple model adaptive control method can achieve adaptive control of the GGBS production process, reduce system overshoot and improve the control quality.