Multi-stage Nonlinear Model Predictive Control with verified robust constraint satisfaction
Multi-stage Nonlinear Model Predictive Control with verified robust constraint satisfaction
复制标题
DOI：
10.1109/cdc.2014.7039821
复制
发表时间：
2014-12
期刊：
影响因子：
--
通讯作者：
S. Lucia;R. Paulen;S. Engell
中科院分区：
文献类型：
--
作者：
S. Lucia;R. Paulen;S. Engell
提出了一种在多阶段非线性模型预测控制（NMPC）框架下利用动态状态定界技术验证鲁棒约束满足的方法。在多级NMPC中，不确定性由离散场景树描述，并且未来的控制输入取决于不确定性的先前实现，构成闭环方法，该方法已被证明比开环方法提供显著更好的性能。虽然该方法在实践中已经证明了非常有前途的结果，多阶段NMPC的问题之一是，不能保证的不确定性值，没有明确考虑的情况树。在这项工作中，我们解决这个问题，通过更新所产生的优化问题，在迭代的方式，使约束的满足是保证严格的边界上的状态变量的一组可能的不确定性实现的基础上。我们说明，该方法可以处理在真实的时间与具有挑战性的问题，提出了工业间歇聚合反应器的模拟结果。
This paper presents an approach to verify robust constraint satisfaction using dynamic state bounding techniques in the framework of multi-stage Nonlinear Model Predictive Control (NMPC). In multi-stage NMPC, the uncertainty is described by a tree of discrete scenarios, and the future control inputs depend on the previous realizations of the uncertainty, constituting a closed-loop approach which has been shown to provide significantly better performance than an open-loop approach. While the approach has demonstrated very promising results in practice, one of the problems of multi-stage NMPC is the fact that no guarantees can be given for the uncertainty values that are not explicitly considered in the scenario tree. In this work, we address this problem by updating the resulting optimization problem in an iterative fashion such that the constraints satisfaction is guaranteed based on the rigorous bounding of the state variables over the set of possible uncertainty realizations. We illustrate that the approach can deal in real time with challenging problems by presenting simulation results of an industrial batch polymerization reactor.