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math.OC2026
Closed-loop analysis of linear stochastic MPC with risk-averse constraints
Jonas Schießl, Ruchuan Ou, Michael H. Baumann +2
Chance constraints are widely used in stochastic model predictive control (MPC) to enforce probabilistic state and input constraints in the presence of unbounded disturbances. Howe…
math.OC2025
Stability and performance of stochastic economic MPC -- Stochastic characterization of the closed-loop asymptotics
Jonas Schießl, Hannah Selder, Ruchuan Ou +3
Model Predictive Control (MPC) is well understood in the deterministic setting, yet rigorous stability and performance guarantees for stochastic MPC remain limited to the considera…
math.OC2025
Towards turnpike-based performance analysis of risk-averse stochastic predictive control
Jonas Schießl, Ruchuan Ou, Michael H. Baumann +2
In this paper, we present performance estimates for stochastic economic MPC schemes with risk-averse cost formulations. For MPC algorithms with costs given by expectations, it was…