The research of our group is concerned with
- Control and complex dynamical systems
and is organized in three main pillars:
- Hybrid Systems and Cyberphysics
- Complex Dynamical Systems
- Machine Learning and Control.
The first central research goal is development of theoretical bases for a holistic approach to control systems in the cyber-physical domain, this invoking conceptual and technical interplay between control, communication, and information theories. In our group, we develop instances of this unifying theory by utilizing hybrid dynamical theory in its algebraic (scheduling) and symbolical (coding) formulations.
The second main pillar refers to dynamical systems, involving optimal control of partial differential equations and stochastic control with applications in chemical engineering and systems biology. We are interested in stability of various classes of nonlinear dynamical systems (impulsive differential equations, hybrid and switched systems, etc.) involving analytical and computer algebra methods.
The third research subject concerns cooperative reinforcement learning with applications in multi-agent systems and collaborative robotics.
Applications include cyber-physical engineering problems s.a. distributed/cooperative control, event-based control, asynchronous scheduling a. control (collaborative robotics and vehicles and Industry 4.0), as well as particulate systems (in crystallization) and cancer modeling.