5 papers
Aggressiveness-Aware Learning-based Control of Quadrotor UAVs with Safety Guarantees
Leonardo Colombo, Thomas Beckers, Juan Giribet
This paper presents an aggressiveness-aware control framework for quadrotor UAVs that integrates learning-based oracles to mitigate the effects of unknown disturbances. Starting fr…
CBDs: Differentiable Causal Block Diagrams
Thomas Beckers, Ján DrgoÅa, Truong X. Nghiem
Modern cyber-physical systems (CPS) integrate physics, computation, and learning, demanding modeling frameworks that are simultaneously composable, learnable, and verifiable. Yet e…
Inference in Latent Force Models Using Optimal State Estimation
Tobias M. Wolff, Victor G. Lopez, Matthias A. Müller +1
Latent force models, a class of hybrid modeling approaches, integrate physical knowledge of system dynamics with a latent force - an unknown, unmeasurable input modeled as a Gaussi…
Safe Physics-Informed Machine Learning for Dynamics and Control
Jan Drgona, Truong X. Nghiem, Thomas Beckers +6
This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models…
Physics-informed Learning for Passivity-based Tracking Control
Thomas Beckers, Leonardo Colombo
Passivity-based control ensures system stability by leveraging dissipative properties and is widely applied in electrical and mechanical systems. Port-Hamiltonian systems (PHS), in…