4 papers indexed
Matteo Tomasetto, Francesco Braghin, J. Nathan Kutz, Andrea Manzoni
Controlling dynamical systems in real-time across multiple scenarios is critical to enabling adaptive control strategies, ensuring stability and efficiency. However, to tailor control actions in response to varying scenarios, traditional optimal control problems typically require…
Nicolò Botteghi, Gabriele Pascali, Urban Fasel, Andrea Manzoni
In this work we investigate reinforcement learning (RL) as a framework for the robust control of parametrized dynamical systems in presence of measurements and model uncertainties. High-dimensional state spaces, expensive numerical solvers, the partial knowledge of the governing…
Matteo Tomasetto, Nicolò Botteghi, Gabriele Bruni, Andrea Manzoni
Reinforcement learning (RL) has recently emerged as a promising feedback control strategy for nonlinear and complex dynamical systems. However, RL algorithms are sample inefficient and require a large number of interaction with the environment to synthesize optimal control strate…
Andrea Maria Braghin, Nicolò Botteghi, Matteo Tomasetto, Andrea Manzoni, Gabriele Cazzulani
Autonomous robotic navigation in nonstationary time-varying fluid flows remains a fundamental challenge due to partial observability and the unpredictability of realistic environments. While classical optimal control frameworks employed in robotics require unrealistic a-priori gl…