Automated Synthesis of Stochastic Cyber-Physical Systems: A Robust Approach

ongoing

Cyber-physical systems (CPS) are complex systems with tight interactions between cyber elements and physical components. The cyber elements are control algorithms implemented by computer-based software. Developing the embedded control software for CPS is currently ad hoc and error-prone, which has created costly undesired behaviours, particularly in safety-critical applications. Examples of such undesired behaviours include frequency deviation in power networks causing outages or blackouts (e.g., in Jan 2021 in EU, affecting 200k households), crash of airplanes due to software bugs (Boeing 737 Max, costs 15.9 billion euros) or autonomous cars having software bugs (Toyota recalled 65,000 cars in 2015). Nowadays, most of the costs of CPS design is spent on ensuring that the system meets all the requirements especially when it is working in uncertain conditions. In order to design reliable CPS and to reduce the costs of such a design, I propose a novel robust synthesis approach that computes automatically the control software from high-level requirements. This novel approach creates a paradigm shift in CPS design as it computes control software in a push-button manner and eliminates time-consuming, costly post-validation steps. The approach tackles the CPS complexity by developing new abstraction techniques that are compositional and robust to model uncertainties, which will be integrated in a unified framework for automating the design of the control software. This synthesis paradigm is founded on novel compositional similarity relations with coupled uncertainties and coupled computations on abstract models. My project is high-risk because it requires merging and re-thinking different design methodologies from multiple disciplines including control theory, computer science, and probability theory. It is high-gain since it will transform the design principles of CPS to enable designing large-scale yet reliable and safe CPS working autonomously in uncertain conditions.