Data-driven simulation and control

By Ivan Markovsky, Paolo Rapisarda. International Journal of Control, 2008

349 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b0d1701cd4cbc2981c0da816d4404ef1505b7305

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In Control and Systems Engineering

In Statistical and Nonlinear Physics

Citations in the corpus

Behavioral systems theory in data-driven analysis, signal processing, and control (2021, methodology): “The algorithms for datadriven simulation and control of (Markovsky and Rapisarda, 2008), which are a follow-up of the ones in Markovsky et al.”
From Noisy Data to Feedback Controllers: Nonconservative Design via a Matrix S-Lemma (2020, background): “Thelatter lineofworkhasbeenquitefruitful,withcontributionsranging fromoutputmatching[34]andcontrolbyinterconnection[35], to data-enabled predictive control [36] and a data-based closed-loop system parameterization [37].”
Data-Enabled Predictive Control: In the Shallows of the DeePC (2018, methodology): “Instead, similar to [10], we approach the problem from a behavioural systems theory perspective [12].”
On Lyapunov functions and data-driven dissipativity (2017, background): “In recent developments of data-based approaches to control (Shi and Skelton (2000); Safonov and Tsao (1997); Maupong and Rapisarda (2016, 2017); Markovsky and Rapisarda (2008)), where the design of a controller is based on system data rather than models, which are in many real-life situations not…”
Data-Driven Model Predictive Control With Stability and Robustness Guarantees (2019, methodology): “Although this result has found various applications in the field of system identification [13]–[15], it has only recently been used to develop data-driven methods for system analysis and control with theoretical guarantees.”
From model-based control to data-driven control: Survey, classification and perspective (2013, background): “In literatures, the subspace approach [66,77,98], the data space approach [38,69,103] and the data-driven simulation approach [89–91] have been shown to share the idea that system dynamics are represented as a subspace of a finite-dimensional vector space, which consists of the time series data of i”
Bridging Direct and Indirect Data-Driven Control Formulations via Regularizations and Relaxations (2021, methodology): “The latter methods have first been established for deterministic LTI systems in [38] and [39] and have recently been extended by suitably regularizing the optimal control problems.”
Data Informativity: A New Perspective on Data-Driven Analysis and Control (2019, background): “ that the system is representedimplicitly by the so-called Hankel matrix of a measured trajectory. This framework has led to several interesting control strategies, ﬁrst in a behavioral setting [14], [34], [35], and more recently in the context of state-space systems [22], [31], [32], [36]–[38]. The”