activity
20242026
collaborators

16 papers

math.OC2026

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…

math.OC2026

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…

eess.SY2026

A Stochastic Fundamental Lemma with Reduced Disturbance Data Requirements

Ruchuan Ou, Guanru Pan, Timm Faulwasser

Recently, the fundamental lemma by Willems et al. has been extended towards stochastic LTI systems subject to process disturbances. Using this lemma requires previously recorded da…

eess.SY2026

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…

eess.SY2026

System-Theoretic Analysis of Dynamic Generalized Nash Equilibria -- Turnpikes and Dissipativity

Sophie Hall, Florian Dörfler, Timm Faulwasser

Generalized Nash equilibria are used in multi-agent control applications to model strategic interactions between agents that are coupled in the cost, dynamics, and constraints, and…

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…