Control and estimation across domains
This theme brings together two side projects completed during my doctoral coursework. Although they address different application areas, both explore how models, uncertainty, and data can support better decisions in complex systems.
Project areas
- Energy management and control: reinforcement-learning-based strategies for coordinating renewable generation, storage, and loads in microgrids.
- Probabilistic sensor fusion: Bayesian and particle-filter approaches for combining heterogeneous measurements and improving robotic state estimation under uncertainty.
Together, these projects connect intelligent control with probabilistic estimation across energy and robotic systems.