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Found 6 papers, 0 papers with code
Data-driven Output Regulation via Gaussian Processes and Luenberger Internal Models
This paper deals with the problem of adaptive output regulation for multivariable nonlinear systems by presenting a learning-based adaptive internal model-based design strategy.
Adaptive Nonlinear Regulation via Gaussian Process
The paper deals with the problem of output regulation of nonlinear systems by presenting a learning-based adaptive internal model-based design strategy.
Output Regulation by Postprocessing Internal Models for a Class of Multivariable Nonlinear Systems
In this paper we propose a new design paradigm, which employing a postprocessing internal model unit, to approach the problem of output regulation for a class of multivariable minimum-phase nonlinear systems possessing a partial normal form.
A Distributed Methodology for Approximate Uniform Global Minimum Sharing
Moreover, a global and uniform (both in the initial time and in the initial conditions) asymptotic stability result is provided towards a steady state which can be made arbitrarily close to the sought minimum.
About Robustness of Control Systems Embedding an Internal Model
Robustness is a basic property of any control system.
Stable Neural Flows
We introduce a provably stable variant of neural ordinary differential equations (neural ODEs) whose trajectories evolve on an energy functional parametrised by a neural network.
