The research of our group is concerned with
- Control and 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 reinforcement learning, classification and generative algorithms with applications to autonomous and multi-agent systems.
In addition to control theory, we are engaged in modeling, analysis and algorithms design for dynamical systems in various scientific and engineering disciplines, including:
- Cyberphysical systems: Time-critical embedded systems involving a tight interaction between control and communication systems
- Autonomous and cooperative systems: Autonomous driving, mobile robots, and cooperative robot arms
- Energy and demand-side management: Distributed active grids and management of load flexibility for facilitating stability of power supply systems
- Smart logistics and production: Dynamic management of smart autonomous and cooperating machines and robots for increased and flexible productivity and supply
- Particles, populations and process control: Modeling, optimization and control of population dynamics in cancer, crystallization and granulation
- Computational systems biology: Modeling, inference and analysis of signaling and controlling subcellular pathways.