12 citations · 20 across the 28 of their papers we have counts for
5 papers · 1 filter
Non-asymptotic Bounds of Learning-based Linear MPC With Input Constraints and Unbounded Stochastic Noise
Changyi Lei, Seth Siriya, Dragan Nešić +1
This paper studies learning-based model predictive control (MPC) for stabilizing unknown discrete-time linear systems with hard input constraints and additive unbounded sub-Gaussia…
Learning-based Homothetic Tube MPC with Non-Asymptotic Guarantees
Changyi Lei, Seth Siriya, Dragan Nešić +1
This paper studies learning-based MPC for constrained stabilization of discrete-time linear systems with unknown system parameters and additive bounded disturbances. We develop a t…
On robustness, input-to-state stability and backstepping for stochastic differential equations
Robert H. Moldenhauer, Dragan Nešić, Mathieu Granzotto +2
We study conditions under which stability of the origin of stochastic differential equations is robust to small perturbations. We express robustness in two ways, firstly in the sen…
Value iteration with stopping criterion: finite iterations, stability, and near-optimality guarantees
Mathieu Granzotto, Romain Postoyan, Dragan Nešić +2
Value iteration (VI) is a cornerstone of dynamic programming that allows computing near-optimal feedback laws for general plant dynamics and cost functions. In practice, however, i…
Discounted MPC and infinite-horizon optimal control under plant-model mismatch: Stability and suboptimality
Robert H. Moldenhauer, Karl Worthmann, Romain Postoyan +2
We study closed-loop stability and suboptimality for MPC and infinite-horizon optimal control solved using a surrogate model that differs from the real plant. We employ a unified f…