Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty
Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty
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DOI：
10.1016/j.jprocont.2013.08.008
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发表时间：
2013-10-01
影响因子：
4.2
通讯作者：
Engell, Sebastian
中科院分区：
文献类型：
--
作者：
Lucia, Sergio;Finkler, Tiago;Engell, Sebastian
模型预测控制（MPC）由于其能够处理多输入多输出对象和约束条件，已成为过程工业中最流行的控制技术之一。然而，在模型的不确定性和干扰的存在下，其性能可能会恶化。因此，鲁棒MPC技术的发展在过去几年中得到了广泛的讨论，但由于方法的保守性或计算复杂性，它们很少（如果有的话）应用于实践。在本文中，我们提出了多级NMPC作为一个有前途的鲁棒非保守非线性模型预测控制方案。该方法是基于一个场景树的不确定性的演变表示，并导致一个非保守的不确定性植物的鲁棒控制，因为未来的输入适应新的信息被考虑在内。仿真结果表明，在存在不确定性的情况下，多阶段NMPC的性能优于标准NMPC和min-max NMPC。此外，该方法的优点示出的情况下，只有噪声的测量是可用的和不可测的状态和不确定性必须使用一个观察器估计。结果表明，它可以实现更好的性能比通过在线估计未知参数和适应的植物模型。(C)2013爱思唯尔有限公司保留所有权利。
Model predictive control (MPC) has become one of the most popular control techniques in the process industry mainly because of its ability to deal with multiple-input multiple-output plants and with constraints. However, in the presence of model uncertainties and disturbances its performance can deteriorate. Therefore, the development of robust MPC techniques has been widely discussed during the last years, but they were rarely, if at all, applied in practice due to the conservativeness or the computational complexity of the approaches. In this paper, we present multi-stage NMPC as a promising robust non-conservative nonlinear model predictive control scheme. The approach is based on the representation of the evolution of the uncertainty by a scenario tree, and leads to a non-conservative robust control of the uncertain plant because the adaptation of future inputs to new information is taken into account. Simulation results show that multi-stage NMPC outperforms standard and min-max NMPC under the presence of uncertainties for a semi-batch polymerization benchmark problem. In addition, the advantages of the approach are illustrated for the case where only noisy measurements are available and the unmeasured states and the uncertainties have to be estimated using an observer. It is shown that better performance can be achieved than by estimating the unknown parameters online and adapting the plant model. (C) 2013 Elsevier Ltd. All rights reserved.