Model predictive control is a high-performance control method, but it incurs comparatively high online computational costs across many control tasks and is challenging to realize in low-level hardware...
arXiv:2607. 07252v1 Announce Type: new Abstract: Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics.
By Georg Sch\"afer, Jakob Rehrl, Stefan Huber, Simon Hirlaender
Reinforcement learning (RL) enables the synthesis of control policies directly from data, making it highly appealing for complex cyber-physical systems (CPSs) and robotics. A persistent challenge, however, is ensuring strict, hard safety constraints during the active learning phase.
The paper introduces A‑SLS, a learning‑aware robust adaptive model predictive control framework that forecasts future parameter uncertainty and preserves correlations among disturbances, parameter errors, and estimate updates to build tight reachable sets. It presents a GPU‑accelerated solver enabling real‑time performance on high‑dimensional systems, demonstrated on a quadruped with 61 states and a Crazyflie drone that learns dynamics online to stabilize during flight.
By Anutam Srinivasan, Siddhant Panse, Glen Chou
arXiv:2608. 10332v1 Announce Type: cross Abstract: Differentiable predictive control (DPC), a self-supervised learning approach for approximating explicit model predictive control (MPC) policies, offers significant computational advantages over online optimization-based MPC.
By Guangyu Wu, J\'an Drgo\v{n}a
arXiv:2606. 30935v1 Announce Type: cross Abstract: While neural network control policies are powerful, their deployment on safety critical systems depends on ensuring that they obey strict constraints.
By Long Kiu Chung, Shreyas Kousik