10 papers
Dissipativity-Based Data-Driven Decentralized Control of Interconnected Systems
Taiki Nakano, Ahmed Aboudonia, Jaap Eising +3
We propose data-driven decentralized control algorithms for stabilizing interconnected discrete-time linear time-invariant systems. We first derive a data-driven condition to synth…
Choose Wisely: Data-driven Predictive Control for Nonlinear Systems Using Online Data Selection
Joshua Näf, Keith Moffat, Jaap Eising +1
This paper proposes Select-Data-driven Predictive Control (Select-DPC), a new method for controlling nonlinear systems using output-feedback for which data are available but an exp…
Convergence Analysis of Distributed Optimization: A Dissipativity Framework
Aron Karakai, Jaap Eising, Andrea Martinelli +1
We develop a system-theoretic framework for the structured analysis of distributed optimization algorithms with decomposable cost functions. We model such algorithms as a network o…
Stability, Contraction, and Controllers for Affine Systems
L. P. Wieringa, A. Padoan, F. Dorfler +1
Recent developments in data-driven control have revived interest in the behavioral approach to systems theory, where systems are defined as sets of trajectories rather than being d…
Soft projections for robust data-driven control
András Sasfi, Jaap Eising, Florian Dörfler
We consider data-based predictive control based on behavioral systems theory. In the linear setting this means that a system is described as a subspace of trajectories, and predict…
On analysis of open optimization algorithms
Jaap Eising, Florian Dörfler
We consider optimization algorithms that are open systems, that is, with external inputs and outputs. Such algorithms arise for instance, when analyzing the effect of noise or dist…