6 papers
Towards turnpike-based performance analysis of risk-averse stochastic predictive control
Jonas SchieÃl, 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…
PolyOCP.jl -- A Julia Package for Stochastic OCPs and MPC
Ruchuan Ou, Learta Januzi, Jonas SchieÃl +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…
Separable Approximations of Optimal Value Functions and Their Representation by Neural Networks
Mario Sperl, Luca Saluzzi, Dante Kalise +1
The use of separable approximations is proposed to mitigate the curse of dimensionality related to the approximation of high-dimensional value functions in optimal control. The sep…
Turnpike and dissipativity in generalized discrete-time stochastic linear-quadratic optimal control
Jonas SchieÃl, 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…
A Polynomial Chaos Approach to Stochastic LQ Optimal Control: Error Bounds and Infinite-Horizon Results
Ruchuan Ou, Jonas SchieÃl, 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…
Separable approximations of optimal value functions under a decaying sensitivity assumption
Mario Sperl, Luca Saluzzi, Lars Grüne +1
An efficient approach for the construction of separable approximations of optimal value functions from interconnected optimal control problems is presented. The approach is based o…