D
Daniel Sbarbaro
Researcher at University of Concepción
Publications - 154
Citations - 2184
Daniel Sbarbaro is an academic researcher from University of Concepción. The author has contributed to research in topics: Control theory & Nonlinear system. The author has an hindex of 22, co-authored 154 publications receiving 1791 citations. Previous affiliations of Daniel Sbarbaro include SERC Reliability Corporation.
Papers
More filters
Journal ArticleDOI
Multiobjective Switching State Selector for Finite-States Model Predictive Control Based on Fuzzy Decision Making in a Matrix Converter
Felipe Villarroel,Jose Espinoza,Christian A. Rojas,Jose Rodriguez,Marco Rivera,Daniel Sbarbaro +5 more
TL;DR: The standard selection stage is replaced by a fuzzy decision-making strategy, considering, as a case study, the control of both load and supply currents in the direct matrix converter (DMC), and a simple selection scheme is obtained.
Journal ArticleDOI
Irreversible port-Hamiltonian systems: A general formulation of irreversible processes with application to the CSTR
TL;DR: In this paper, the authors proposed a lift of the reversible port-hammian system to control contact systems defined on the Thermodynamic Phase Space, which is canonically endowed with a contact structure associated with Gibbs' relation.
Journal ArticleDOI
Design of a Discrete-Time Linear Control Strategy for a Multicell UPQC
Javier Munoz,Jose Espinoza,Carlos R. Baier,Luis Moran,Eduardo Espinosa,Pedro Melin,Daniel Sbarbaro +6 more
TL;DR: A discrete-time linear control strategy for a multilevel three-phase unified power quality conditioner (UPQC) based on single-phase power cells is presented and a dedicated local control strategy is proposed to ensure a symmetrical distribution of the power among the power cells.
Journal ArticleDOI
An adaptive sliding-mode controller for discrete nonlinear systems
D. Munoz,Daniel Sbarbaro +1 more
TL;DR: A sliding-mode controller for a class of nonlinear discrete-time systems using a modified switching function that produces a low-chattering control signal and an adaptive term is added to the original sliding- mode algorithm.
Nonlinear adaptive control using non-parametric Gaussian Process prior models
TL;DR: Nonparametric Gaussian Process prior models, taken from Bayesian statistics methodology are used to implement a nonlinear adaptive control law, which leads to implicit regularisation of the control signal (caution), and excitation of the system.