分散式MPC经济性能评估

近年来,学术界对集中式模型预测控制 (Model predictive control, MPC) 性能评估进行了广泛的研究. 对于大规模化工过程, 工业现场通常采用分散式MPC的控制结构. 由于各子系统间存在复杂的耦合关系, 针对集中式MPC 的性能评估方法不能客观反映分散式MPC的性能. 本文基于线性矩阵不等式(Linear matrix inequality, LMI)的方法对分散式MPC进行经济性能评估. 首先提出了一种迭代方法求解分散式线性二次型调节器(Linear quadratic regulator, LQR)问题, 该方法显著降低了已有求解方法的保守性. 再利用LQR基准建立了一组随机优化命题对MPC进行经济性 能评估, 评估方法对集中式MPC与分散式MPC均适用, 评估结果可以指导MPC参数调整, 也可以为集中式与分散式MPC结构选择提供重要参考. 通过对重油分馏塔控制问题的仿真验证了本文方法的有效性与应用价值.

Economic Performance Assessment of Decentralized Model Predictive Control

In recent years, performance assessment of centralized model predictive control (MPC) has attracted a lot of academic interest. In fact, large-scale complex chemical processes are usually controlled by decentralized MPC in the industrial field. Due to the complex interactions between subsystems, most performance assessment methods regarding centralized control structure cannot be applied to the performance assessment of decentralized MPC. In this paper, a linear matrix inequality (LMI) based approach is proposed to assess the economic performance of decentralized MPC. First, an iterative method is proposed to obtain the decentralized linear quadratic regulator (LQR) benchmark. A probabilistic optimization problem is then formulated to assess the economic performance of MPC. The proposed approach can be applied to assessment of both centralized MPC and decentralized MPC. The assessment result provides guidance for parameter tuning as well as control structure selection. The simulation result of Shell control problem shows the effectiveness of the proposed method.