#robustcontrol

Scenario-Based Robust Controller Design

This work proposes a novel data-driven control approach for linear systems with probabilistic parameter variations. Unlike traditional methods that design controllers for a single system, our method uses trajectory data from multiple system instances to synthesize a single robust state-feedback controller. Leveraging the scenario approach, we provide probabilistic guarantees that the controller will stabilize unseen variations drawn from the same distribution. This enables scalable and generalizable control design without requiring exhaustive data collection from all possible system configurations—ideal for applications like robotics fleets, power systems, and autonomous vehicles operating under uncertainty.

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