58 citations · 86 across the 13 of their papers we have counts for
16 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…
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…
Near-optimal performance of stochastic economic MPC
Jonas Schießl, Ruchuan Ou, Timm Faulwasser +2
This paper presents first results for near optimality in expectation of the closed-loop solutions for stochastic economic MPC. The approach relies on a recently developed turnpike…
A Polynomial Chaos Approach to Stochastic LQ Optimal Control: Error Bounds and Infinite-Horizon Results
Ruchuan Ou, Jonas Schießl, Michael Heinrich Baumann +2
The stochastic linear--quadratic regulator problem subject to Gaussian disturbances is well known and usually addressed via a moment-based reformulation. Here, we leverage polynomi…
Turnpike and dissipativity in generalized discrete-time stochastic linear-quadratic optimal control
Jonas Schießl, Ruchuan Ou, Timm Faulwasser +2
We investigate different turnpike phenomena of generalized discrete-time stochastic linear-quadratic optimal control problems. Our analysis is based on a novel strict dissipativity…
Pathwise turnpike and dissipativity results for discrete-time stochastic linear-quadratic optimal control problems
Jonas Schießl, Ruchuan Ou, Timm Faulwasser +2
We investigate pathwise turnpike behavior of discrete-time stochastic linear-quadratic optimal control problems. Our analysis is based on a novel strict dissipativity notion for su…
Towards data-driven stochastic predictive control
Guanru Pan, Ruchuan Ou, Timm Faulwasser
Data-driven predictive control based on the fundamental lemma by Willems et al. is frequently considered for deterministic LTI systems subject to measurement noise. However, little…
Data-driven Stochastic Output-Feedback Predictive Control: Recursive Feasibility through Interpolated Initial Conditions
Guanru Pan, Ruchuan Ou, Timm Faulwasser
The paper investigates data-driven output-feedback predictive control of linear systems subject to stochastic disturbances. The scheme relies on the recursive solution of a suitabl…
On Data-Driven Stochastic Output-Feedback Predictive Control
Guanru Pan, Ruchuan Ou, Timm Faulwasser
The fundamental lemma by Jan C. Willems and co-authors enables the representation of all input-output trajectories of a linear time-invariant system by measured input-output data.…
Behavioral Theory for Stochastic Systems? A Data-driven Journey from Willems to Wiener and Back Again
Timm Faulwasser, Ruchuan Ou, Guanru Pan +2
The fundamental lemma by Jan C. Willems and co-workers, which is deeply rooted in behavioral systems theory, has become one of the supporting pillars of the recent progress on data…
On a Stochastic Fundamental Lemma and Its Use for Data-Driven Optimal Control
Guanru Pan, Ruchuan Ou, Timm Faulwasser
Data-driven control based on the fundamental lemma by Willems et al. is frequently considered for deterministic LTI systems subject to measurement noise. However, besides measureme…
A Simulation Study on Turnpikes in Stochastic LQ Optimal Control
Ruchuan Ou, Michael Heinrich Baumann, Lars Grüne +1
This paper presents a simulation study on turnpike phenomena in stochastic optimal control problems. We employ the framework of Polynomial Chaos Expansions (PCE) to investigate the…
ALADIN- -- An open-source MATLAB toolbox for distributed non-convex optimization
Alexander Engelmann, Yuning Jiang, Henrieke Benner +3
This paper introduces an open-source software for distributed and decentralized non-convex optimization named ALADIN-. ALADIN- is a MATLAB implementation of tailored variants…