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Dionysos is the software of the ERC project Learning to control (L2C). In view of the Cyber-Physical Revolution, the only sensible way of controlling these complex systems is often by discretizing the different variables, thus transforming the model into a simple combinatorial problem on a finite-state automaton, called an abstraction of this system. The goal of L2C is to transform this approach into an effective, scalable, cutting-edge technology that will address the CPS challenges and unlock their potential. This ambitious goal will be achieved by leveraging powerful tools from Mathematical Engineering.
The current version is still in the making, and allows to solve problems such as reachability problems for hybrid systems. See the Docs for further information.
Rather than relying on closed-form analysis of a model of the dynamical system, Dionysos will learn the optimal control from data, whether harvested from the physical system or generated synthetically. It will rely on a novel methodology, combining the efficiency of several modern optimization/control-theoretic/machine-learning techniques with the theoretical power of the Abstraction approach. All the pieces of the architecture are chosen to foster black-box and data-driven analysis, thereby matching rising and unresolved challenges. Summarizing, the objectives are
- To develop a mathematical and algorithmic framework for efficient Abstraction of Cyber-Physical Systems thriving on recent technologies in Optimization and Control;
- To leverage this framework in situations where the system is described by data, rather than a classical model;
Download Julia, and follow the instructions described here.