Thesis of Quentin Possamaï

Deep Learning and Control Theory for stable Drone Control


In the context of control of agents like UAVs (drones) and mobile robots, this PhD position addresses fundamental contributions on the crossroads between Artificial Intelligence (AI) / Machine Learning (ML) and Control Theory (CT). The two fields, while being distinct, have a long history of interactions between them and as both fields mature, their overlap is more and more evident. CT aims to provide differential model-based approaches to solve stabilization and estimation problems. These model-driven approaches are powerful because they are based on a thorough understanding of the system and can leverage established physical relationships. However, nonlinear models usually need to be simplified and they have difficulty accounting for noisy data and non modeled uncertainties.


Machine Learning, on the other hand, aims at learning complex models from (often large amounts of) data and can provide data-driven models for a wide range of tasks. Markov Decision Processes (MDP) and Reinforcement Learning (RL) have traditionally provided a mathematically founded framework for control applications, where agents are required to learn policies from past interactions with an environment. In recent years, this methodology has been combined with deep neural networks, which play the role of high-capacity function approximators, and model the discrete or continuous policy function or a function of the accumulated reward of the agent, or both.

The PhD project proposes fundamental research with planned algorithmic contributions on the integration of models, prior knowledge and learning in control and the perception action cycle:

  • data-driven learning and identification of physical models for control;
  • state representation learning for control (learning disentangled representations for control with structured deep learning);
  • stable control using hybrid methods using machine learning and control theory.

Advisor: Christian Wolf