Robust Control
Overview
Robust control studies how to keep dynamical systems stable, safe, and effective when their models are imperfect or conditions change. Common approaches include H-infinity, sliding-mode and adaptive control, tube-based and nonlinear model predictive control, and Lyapunov, invariant-set, reachability, or control-barrier-function methods. Recent work also combines these tools with disturbance observers, learned dynamics, and bounded reinforcement-learning corrections, testing systems such as quadrotors, manipulators, vehicles, and multi-robot teams in simulation and on hardware. A central challenge is balancing formal guarantees against conservatism and computation, while ensuring that learned or adaptive controllers remain reliable under shifts not covered by their training or models.
Latest papers
142 papers15 papers in the last four weeks, down 12% on the four weeks before. 0.1% of all new papers.