We propose a robust adaptive Model Predictive Control (MPC) strategy with online set-based estimation for constrained linear systems with unknown parameters and bounded disturbances. A sample-based test applied to predicted trajectories is used to ensure convergence of parameter estimates by enforcing a persistence of excitation condition on the closed loop system. The control law robustly satisfies constraints and has guarantees of feasibility and input-to-state stability. Convergence of parameter set estimates to the actual system parameter vector is guaranteed under conditions on reachability and tightness of disturbance bounds.
@article{arxiv.2211.12478,
title = {Adaptive robust predictive control with sample-based persistent excitation},
author = {Xiaonan Lu and Mark Cannon},
journal= {arXiv preprint arXiv:2211.12478},
year = {2023}
}