16 citations · 18 across the 14 of their papers we have counts for
8 papers · 1 filter
Bounded Linear Programs for Data-Driven Optimal Control via Moment-Matching
Andrea Martinelli, Lucia Pezzetti, Niklas Schmid +2
Linear programming (LP) formulations offer a conceptually elegant approach to infinite-horizon, model-free nonlinear optimal control in continuous spaces. However, in addition to t…
Split-as-a-Pro: behavioral control via operator splitting and alternating projections
Yu Tang, Carlo Cenedese, Alessio Rimoldi +3
The paper introduces Split-as-a-Pro, a control framework that integrates behavioral systems theory, operator splitting methods, and alternating projection algorithms. The framework…
DeePC-Hunt: Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization
Michael Cummins, Alberto Padoan, Keith Moffat +2
This paper introduces Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization (DeePC-Hunt), a backpropagation-based method for automatic hyperparamete…
Contractivity and linear convergence in bilinear saddle-point problems: An operator-theoretic approach
Colin Dirren, Mattia Bianchi, Panagiotis D. Grontas +2
We study the convex-concave bilinear saddle-point problem , where both, only one, or none of the functions and are strongly convex, a…
Data-Driven Distributionally Robust System Level Synthesis
Francesco Micheli, Anastasios Tsiamis, John Lygeros
We present a novel approach for the control of uncertain, linear time-invariant systems, which are perturbed by potentially unbounded, additive disturbances. We propose a \emph{dou…
Towards a Systems Theory of Algorithms
Florian Dörfler, Zhiyu He, Giuseppe Belgioioso +3
Traditionally, numerical algorithms are seen as isolated pieces of code confined to an {\em in silico} existence. However, this perspective is not appropriate for many modern compu…
Distributionally Robust Infinite-horizon Control: from a pool of samples to the design of dependable controllers
Jean-Sébastien Brouillon, Andrea Martin, John Lygeros +2
We study control of constrained linear systems with only partial statistical information about the uncertainty affecting the system dynamics and the sensor measurements. Specifical…
Data-driven representations of conical, convex, and affine behaviors
Alberto Padoan, Florian Dörfler, John Lygeros
The paper studies conical, convex, and affine models in the framework of behavioral systems theory. We investigate basic properties of such behaviors and address the problem of con…