基于修正闭环子空间辨识--分段线性结构的 环管式丙烯聚合反应过程非线性模型预测控

Nonlinear predictive control with modified closed-loop subspace identification-piecewise linear model for double-loop propylene polymerization process

中文关键词  非线性模型预测控制  MSSARX–PWL结构  双环管丙烯聚合反应过程  分段线性化

英文关键词  nonlinear model predictive control  MSSARX–PWL structure  double-loop propylene polymerization process  piece wise linear

中文摘要

      针对环管式聚丙烯生产过程装置多变量、耦合和非线性等特性容易导致过程控制不稳定及质量指标波动问题, 本文提出了一种基于修正闭环子空间辨识–分段线性(MSSARX--PWL)维纳(Wiener)模型结构的非线性模型预测控制算法. 利用修正的闭环子空间辨识方法(MSSARX)辨识对象在闭环工况下的线性状态空间模型, 并将该线性模型与多变量分段线性化(PWL)方法辨识得到的非线性稳态模型结合, 建立双环管丙烯聚合反应动态过程的非线性预测模型, 而后进一步将非线性模型转化为线性模型, 在线性预测控制算法框架下用二次线性规划方法(LQP)优化控制器, 无须用非线性规划方法(NLP)求解. 从双环管丙烯聚合反应过程仿真例子表明, 该算法不仅能保证模型和控制精度, 而且能提高计算效率.

英文摘要

      To solve the unstable and oscillatory problem of the double-loop propylene polymerization process with multivariable, coupling and nonlinearity, we propose a nonlinear model predictive control algorithm based on the Wiener-type model by modified closed-loop subspace identification (MSSARX). A linear state space model under closed-loop conditions and a nonlinear steady-state model are identified by using the modified closed-loop subspace identification method (MSSARX) and the multivariate piecewise linear (PWL) method, respectively. These two models are then combined into an MSSARX–PWL model structure, which is employed as the nonlinear predictive model of the process. To reduce the computational load, this nonlinear model is consequently linearized to ensure that linear quadratic programming (LQP) optimization controller can be applied instead of the nonlinear one. By applying the proposed algorithm, not only the accurate prediction and control are guaranteed, but the computational efficiency is also improved at the same time. The effectiveness of the proposed algorithm is demonstrated on the simulation process of double-loop propylene polymerization.