Control and diagnosis based on learning from data

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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.