Robust Model Predictive Control for Linear Systems with Interval Matrix Model Uncertainty
A novel control scheme uses matrix zonotopes to pre-calculate all uncertainty bounds, slashing online processing time.
Researchers Renato Quartullo, Andrea Garulli, and Mirko Leomanni developed a novel Robust Model Predictive Control (MPC) scheme for systems with interval matrix uncertainty. The key innovation uses matrix zonotopes to create a set-theoretic over-approximation of the system's impulse response. Crucially, all complex uncertainty bounds are computed offline, making the online computational load independent of the number of uncertain parameters. This enables control of high-dimensional systems with multiple uncertainties that were previously intractable.
Why It Matters
Enables real-time, robust control of complex physical systems (like robotics or power grids) where model uncertainty is a major challenge.