Extension of dynamic matrix control to multiple models
Extension of dynamic matrix control to multiple models
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DOI：
10.1016/s0098-1354(03)00038-3
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发表时间：
2003-09
期刊：
影响因子：
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通讯作者：
B. Aufderheide;B. Wayne Bequette
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文献类型：
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作者：
B. Aufderheide;B. Wayne Bequette
本文的目的是扩展动态矩阵控制（DMC），以处理不同的操作制度，并拒绝参数干扰。这是通过两个新的多模型预测控制（MMPC）计划：一个基于实际的阶跃响应测试和其他基于最小知识的一阶加死区时间模型（FOPDT）。这两种方法都不需要基本的建模。作为一个基准比较，这两个控制器相比，使用扩展卡尔曼滤波器（EKF）的非线性模型预测控制器（NL-MPC），没有初始模型/工厂不匹配。应用的例子是等温货车de Vusse反应，它具有挑战性的输入多重性。模拟包括进料浓度、动力学参数和添加剂输入和输出噪声的扰动。这两种控制器具有与NL-MPC相当的性能，并且在多个干扰的情况下可以优于NL-MPC。
The purpose of the paper is to extend dynamic matrix control (DMC) to handle different operating regimes and to reject parameter disturbances. This is done by two new multiple model predictive control (MMPC) schemes: one based on actual step response tests and the other on a minimal knowledge based first order plus dead time models (FOPDT). Both approaches do not require fundamental modeling. As a benchmark comparison, the two controllers are compared with a nonlinear model predictive controller (NL-MPC) using an extended Kalman filter (EKF) with no initial model/plant mismatch. The application example is the isothermal Van de Vusse reaction, which exhibits challenging input multiplicity. Simulations include disturbances in the feed concentration, kinetic parameters, and additive input and output noise. The two controllers have comparable performance to NL-MPC and in the case of multiple disturbances can outperform NL-MPC.