Model Predictive Control with Active Learning under Model Uncertainty: Why, When, and How
Model Predictive Control with Active Learning under Model Uncertainty: Why, When, and How
复制标题
模型不确定性下的主动学习模型预测控制：原因、时间和方式
DOI：
10.1002/aic.16180
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发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
Tor Aksel N. Heirung, Joel A.
中科院分区：
文献类型：
--
作者：
Tor Aksel N. Heirung, Joel A.
最优控制依赖于一个模型，由于对系统的不完全了解和动态随时间的变化，该模型通常是不确定的。在闭环控制下探测系统可以通过产生比正常运行产生的输入输出数据更多的信息来减少模型的不确定性。本文研究了具有主动学习的模型预测控制(MPC)问题，重点讨论了如何在控制行为中引入探测以减少模型不确定性。我们讨论了这个问题中的一些核心理论问题，并展示了在模型参数和结构存在不确定性的情况下，主动学习保持预测控制性能的潜力。仿真结果表明，当系统发生突然变化(如突然发生故障)时，主动学习特别有用，这种变化可能会影响操作的安全性、可靠性和盈利性。《2018美国化学工程学会学报》：3071-3081,2018
Optimal control relies on a model, which is generally uncertain because of incomplete knowledge of the system and changes in the dynamics over time. Probing the system under closed‐loop control can reduce the model uncertainty through generating input‐output data that is more informative than the data generated from normal operation. This paper addresses the problem of model predictive control (MPC) with active learning, with a particular focus on how incorporating probing in the control action can reduce model uncertainty. We discuss some of the central theoretical questions in this problem, and demonstrate the potential of active learning for maintaining MPC performance in the presence of uncertainty in model parameters and structure. Simulation results show that active learning is particularly beneficial when a system undergoes abrupt changes (such as the sudden occurrence of a fault) that can compromise operational safety, reliability, and profitability. © 2018 American Institute of Chemical EngineersAIChE J, 64: 3071–3081, 2018