no code implementations • 19 May 2024 • Siddharth H. Nair, Charlott Vallon, Francesco Borrelli
Multi-Objective Learning Model Predictive Control is a novel data-driven control scheme which improves a system's closed-loop performance with respect to several control objectives over iterations of a repeated task.
no code implementations • 21 Mar 2024 • Charlott Vallon, Mark Pustilnik, Alessandro Pinto, Francesco Borrelli
This paper focuses on the design of hierarchical control architectures for autonomous systems with energy constraints.
no code implementations • 13 May 2021 • Charlott Vallon, Francesco Borrelli
In addition to task-invariant system state and input constraints, a parameterized environment model generates task-specific state constraints, which are satisfied by the stored trajectories.
1 code implementation • 9 Jun 2020 • Monimoy Bujarbaruah, Charlott Vallon, Francesco Borrelli
We propose a control design method for linear time-invariant systems that iteratively learns to satisfy unknown polyhedral state constraints.
no code implementations • L4DC 2020 • Monimoy Bujarbaruah, Charlott Vallon
This paper proposes an Adaptive Stochastic Model Predictive Control (MPC) strategy for stable linear time-invariant systems in the presence of bounded disturbances.
no code implementations • 16 Mar 2019 • Charlott Vallon, Francesco Borrelli
A task decomposition method for iterative learning model predictive control is presented.