4 papers
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…
PolyOCP.jl -- A Julia Package for Stochastic OCPs and MPC
Ruchuan Ou, Learta Januzi, Jonas Schießl +3
The consideration of stochastic uncertainty in optimal and predictive control is a well-explored topic. Recently Polynomial Chaos Expansions (PCE) have received considerable attent…
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…
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…