Control and diagnosis based on learning from data

基于数据学习的控制和诊断

• 批准号: RGPIN-2022-03443
• 负责人: Zhao, Qing
• 金额: $ 2.84万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752325
• 关键词: Control; diagnosis; based; learning; data

There are several reasons why data becomes more essential in decision making. Firstly most systems and processes are initially well calibrated and are operated under well controlled constraints. Mathematical models can then be established and used for control and performance analysis under normal operation conditions. However, with the natural degradation or unexpected conditions, e.g. anomalies, systems deviate from their initially designed conditions, and fidelity of those models deteriorates. It is normally time consuming to develop complete new models. In this case, data becomes important for performance monitoring, fault diagnosis, and updating of the control law. Secondly, when the system is highly complex, mathematical modelling may not even be valid. In this case, data driven modeling becomes almost the only means. Generally speaking, how to learn from multiple sources of data has attracted great attentions from industries and research communities. Based on recent research progress, we will be focused on several important problems centred around data utilization and exploitation in system control and monitoring: 1) In model based control and fault detection methods, robustness of the design approach against model uncertainties is one of the most important considerations. There have been extensive research results on this topic. However, the robustness issue and uncertainties analysis for data driven approaches are not addressed as much as the model based counterpart. How uncertainties in data propagate through the so called "grey- or black-box" models (usually rendered by machine learning methods) deserves better investigation, and is important in measuring the accuracy or confidence of these methods.  2) Another challenge is the fusion of knowledge and data in designing the health monitoring system. Recently, with the wide adoption of machine learning and NN based modelling, data plays a central role in describing the system behavior and estimating/predicting key outputs. However, process knowledge and physics-based models should not be abandoned. How to exploit such knowledge in the data-driven approaches is of interests. 3) To enhance the data connectivity of the existing control systems, research on data driven control has become more active lately. In the so-called direct data driven control, measured data from the system can be directly utilized to compute the control signal without explicit model parameters, which eliminates the model identification step. While there are some new developments on direct data driven control for linear systems, design of control directly from data for more complex systems, such as high dimensional and nonlinear systems, is an open problem. Furthermore, for autonomous and safety critical systems, fault tolerance of the control system is vital. Hence design of data driven fault tolerant control is of particular interests in this proposal.

数据在决策中变得更加重要的原因有很多。首先，大多数系统和流程最初都经过良好校准，并在良好控制的约束下运行。然后可以建立数学模型并用于正常操作条件下的控制和性能分析。然而，随着自然降解或意外条件，例如异常，系统偏离最初设计的条件，并且这些模型的保真度恶化。开发完整的新模型通常非常耗时。在这种情况下，数据对于性能监控、故障诊断和控制律更新就变得非常重要。其次，当系统高度复杂时，数学建模甚至可能无效。在这种情况下，数据驱动建模几乎成为唯一的手段。总的来说，如何从多源数据中学习已经引起了工业界和研究界的高度关注。根据最近的研究进展，我们将重点关注围绕系统控制和监测中的数据利用和开发的几个重要问题：1）在基于模型的控制和故障检测方法中，针对模型不确定性的设计方法的鲁棒性是最重要的考虑因素之一。关于这个主题已经有广泛的研究成果。然而，数据驱动方法的鲁棒性问题和不确定性分析并没有像基于模型的方法那样得到解决。数据中的不确定性如何通过所谓的“灰盒或黑盒”模型（通常由机器学习方法呈现）传播值得更好的研究，并且对于衡量这些方法的准确性或置信度非常重要。  2）另一个挑战是健康监测系统设计中知识和数据的融合。最近，随着机器学习和基于神经网络的建模的广泛采用，数据在描述系统行为和估计/预测关键输出方面发挥着核心作用。然而，过程知识和基于物理的模型不应被放弃。如何在数据驱动的方法中利用这些知识是人们感兴趣的。 3）为了增强现有控制系统的数据连接性，数据驱动控制的研究最近变得更加活跃。在所谓的直接数据驱动控制中，可以直接利用系统的测量数据来计算控制信号，而无需明确的模型参数，从而消除了模型识别步骤。虽然线性系统的直接数据驱动控制有了一些新的发展，但直接从数据设计更复杂系统（例如高维和非线性系统）的控制仍然是一个悬而未决的问题。此外，对于自主和安全关键系统，控制系统的容错能力至关重要。因此，数据驱动的容错控制的设计在该提案中特别令人感兴趣。