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Found 14 papers, 1 papers with code
Learning-based model augmentation with LFRs
This model structure is able to represent many common model augmentation structures, thus unifying them under the proposed model structure.
Decoupling parameter variation from noise: Biquadratic Lyapunov forms in data-driven LPV control
A promising step from linear towards nonlinear data-driven control is via the design of controllers for linear parameter-varying (LPV) systems, which are linear systems whose parameters are varying along a measurable scheduling signal.
A Linear Parameter-Varying Approach to Data Predictive Control
By means of the linear parameter-varying (LPV) Fundamental Lemma, we derive novel data-driven predictive control (DPC) methods for LPV systems.
Direct data-driven control with signal temporal logic specifications
Most control synthesis methods under temporal logic properties require a model of the system, however, identifying such a model can be a challenging task.
Learning Stable and Robust Linear Parameter-Varying State-Space Models
This paper presents two direct parameterizations of stable and robust linear parameter-varying state-space (LPV-SS) models.
Direct data-driven state-feedback control of general nonlinear systems
Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL) systems.
Data-driven Dissipativity Analysis of Linear Parameter-Varying Systems
We derive direct data-driven dissipativity analysis methods for Linear Parameter-Varying (LPV) systems using a single sequence of input-scheduling-output data.
Direct data-driven LPV control of nonlinear systems: An experimental result
The LPV data-driven control design that builds on this representation form uses only measurement data from the nonlinear system and a priori information on a scheduling map that can lead to an LPV embedding of the nonlinear system behavior.
Direct Data-Driven State-Feedback Control of Linear Parameter-Varying Systems
We derive novel methods that allow to synthesize LPV state-feedback controllers directly from a single sequence of data and guarantee stability and performance of the closed-loop system, without knowing the model of the plant.
LPV Modeling of the Atmospheric Flight Dynamics of a Generic Parafoil Return Vehicle
Obtaining models that can be used for control is of utmost importance to ensure the guidance and navigation of spacecraft, like a Generic Parafoil Return Vehicle (GPRV).
Deep-Learning-Based Identification of LPV Models for Nonlinear Systems
The Linear Parameter-Varying (LPV) framework provides a modeling and control design toolchain to address nonlinear (NL) system behavior via linear surrogate models.
Fundamental Lemma for Data-Driven Analysis of Linear Parameter-Varying Systems
Based on the behavioural framework for LPV systems, we prove that one can obtain a result similar to Willems'.
Data-Driven Predictive Control for Linear Parameter-Varying Systems
Based on the extension of the behavioral theory and the Fundamental Lemma for Linear Parameter-Varying (LPV) systems, this paper introduces a Data-driven Predictive Control (DPC) scheme capable to ensure reference tracking and satisfaction of Input-Output (IO) constraints for an unknown system under the conditions that (i) the system can be represented in an LPV form and (ii) an informative data-set containing measured IO and scheduling trajectories of the system is available.
Convex Incremental Dissipativity Analysis of Nonlinear Systems - Extended version
We investigate how stability and performance characterizations of nonlinear systems in the incremental framework are linked to dissipativity, and how general performance characterization beyond the $\mathcal{L}_2$-gain concept can be understood in this framework.
