CRII: CPS: Information-Constrained Cyber-Physical Systems for Supermarket Refrigerator Energy and Inventory Management

Various critical decision-making and control problems associated with engineering and socio-technical systems are subject to uncertainties. Large-scale data collected from the Internet-of-Things and cyber-physical systems can provide information about the probability distribution of these uncertainties, such as product demand in supermarkets. Such distributional information can be used to dramatically improve the performance of closed-loop systems if they adopt appropriate controllers, which reduce the conservativeness of classical techniques, such as robust control. Several concerns have been raised about how best to incorporate the collected data into critical control and decision-making problems. These concerns center on robustness, safety, risk and reliability because the data and statistical models extracted from the data often result in inaccurate distributional information. The use of poor distributional information in constructing a stochastic optimal controller does not guarantee optimality and can even cause catastrophic system behaviors, such as increasing food safety risks in supermarket refrigerators. The proposed research aims to establish a control-theoretic foundation to resolve these issues by allowing distributional errors in statistical models and by providing control strategies that are robust against these errors, with application to joint optimizing refrigerator and inventory controllers in supermarkets.