Model predictive control with active learning for stochastic systems with structural model uncertainty: Online model discrimination
Model predictive control with active learning for stochastic systems with structural model uncertainty: Online model discrimination
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DOI：
10.1016/j.compchemeng.2019.05.012
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发表时间：
2019-09-02
影响因子：
4.3
通讯作者：
Mesbah, Ali
中科院分区：
文献类型：
--
作者：
Heirung, Tor Aksel N.;Santos, Tito L. M.;Mesbah, Ali
结构模型的不确定性在控制设计中很普遍，它来自于对系统的不完全了解或存在不同的动态行为模式，如系统故障和失灵。本文研究了在结构模型不确定性条件下随机非线性系统的模型预测控制。受双重控制的启发，这里提出的具有主动学习的MPC策略可以探测不确定系统，以在一组候选模型中选择最能描述所观察到的闭环系统数据的模型。建议的控制器涉及在线模型选择的基础上估计的模型假设概率和最小化的计算易于处理的措施的预测贝叶斯风险的选择错误。所提出的方法的性能相比，名义MPC没有学习，MPC与被动学习，和一个强大的MPC方法，系统地占结构模型的不确定性，但没有学习机制。非线性生物反应器的仿真结果表明，主动学习可以有显着的优势，在保持足够的控制性能的存在下，结构的不确定性。主动学习对于改善闭环控制下的在线模型判别和主动故障诊断特别有益。(C)2019爱思唯尔有限公司版权所有。
Structural model uncertainty is prevalent in control design and arises from incomplete knowledge of the system or the existence of different modes of dynamic behavior, such as those arising from system faults and malfunctions. This paper addresses control of stochastic nonlinear systems using model predictive control, or MPC, under structural model uncertainty. Inspired by dual control, the MPC strategy with active learning presented here can probe the uncertain system to select, among a set of candidates, the model that best describes the observed closed-loop system data. The proposed controller involves online model selection based on estimation of the model-hypothesis probabilities and minimization of a computationally tractable measure of the predicted Bayes risk of selection error. The performance of the proposed approach is compared to that of nominal MPC with no learning, MPC with passive learning, and a robust MPC approach that systematically accounts for structural model uncertainty but has no learning mechanism. Simulation results on a nonlinear bioreactor demonstrate that active learning can have significant advantages in maintaining adequate control performance in the presence of structural uncertainty. Active learning can be particularly beneficial for improving online model discrimination and active fault diagnosis under closed-loop control. (C) 2019 Elsevier Ltd. All rights reserved.