The objective of this project is to develop novel methodologies based on (multi-agent) reinforcement learning for health-aware control of complex engineering systems. The research will focus on integrating system health and degradation dynamics into control strategies, enabling decision-making that jointly optimizes performance and long-term asset reliability.
The project will explore how reinforcement learning agents can learn control policies that account for operational objectives, physical constraints, and system aging, with a particular emphasis on multi-agent settings where multiple subsystems interact. Physics-informed models and data-driven approaches will be combined to ensure that learned policies are both efficient and consistent with underlying system dynamics.
Applications will include complex industrial and energy systems (e.g., wind turbines or other large-scale infrastructure), where control decisions have a direct impact on system lifetime.
This PhD position is part of an ERC Consolidator Grant, supporting cutting-edge research on health-aware control and intelligent maintenance of complex systems.
