Rosas-Caro, Julio
Main Affiliation
Preferred name
Rosas-Caro, Julio
Official Name
Rosas Caro, Julio César
ORCID
0000-0003-0161-0575
Researcher ID
R-6224-2018
Scopus Author ID
57191227591
145 results
Now showing 1 - 10 of 145
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Item type:Publication, Control Algorithm for an Inverter-Based Virtual Synchronous Generator with Adjustable Inertia(MDPI AG, 2025-09-05); 25 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Single-Output-Filter Double Dual Ćuk Converter(2024); ; ; Posada, Johnny<jats:p>This study introduces an innovative version of a recently studied converter. A Double Dual Ćuk Converter was recently studied with advantages like the possibility of designing it for achieving a low-input current ripple. The proposed converter, called the Improved Double Dual Ćuk Converter, maintains the advantages of the former one, and it is characterized by requiring one less capacitor and inductor than its predecessor. This allows addressing the challenge of optimizing the topology to reduce component count without compromising the operation; this work proposes an efficient design methodology based on theoretical analysis and experimental validation. Results demonstrate that the improved topology not only retains the advantages of the previous version, including high efficiency and robustness, but also enhances power density by reducing the number of components. These advancements open new possibilities for applications requiring compact and efficient power converters, such as renewable energy systems, electric vehicles, and portable power supply systems. This work underscores the importance of continuous innovation in power converter design and lays the groundwork for future research aimed at optimizing converter topologies. A detailed discussion of the operating principles and modeling of the converter is provided. Furthermore, simulation outcomes highlighting differences in steady-state duration, output voltage, input current ripple, and operational efficiency are shared. The results from an experimental test bench are also presented to corroborate the efficacy of the improved converter.</jats:p>Scopus© Citations 1 13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Guest Editorial: Advances in Non‐Isolated DC–DC Converters and Their Applications(Institution of Engineering and Technology (IET), 2026-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Data-Driven Based Control Applied to DC Network Converters for Voltage Bus Stabilization(2020); 1 11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Rapid Single-Phase Blackout Detection Algorithm Based on Clarke–Park Transformations(MDPI AG, 2026-01-19); ; ; Posada, Johnny26 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Adaptive neural strategies for the interactive tracking flight of two aerial vehicles(2018-01-01); 1 23 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Failure mode and effects analysis and sensitivity analysis for a neutral point re-injection multi-pulse voltage source converter(Elsevier BV, 2025-09); ; 23 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Neural Network Trajectory Tracking Control on Electromagnetic Suspension Systems(2023); Carlos Sotelo<jats:p>A new adaptive-like neural control strategy for motion reference trajectory tracking for a nonlinear electromagnetic suspension dynamic system is introduced. Artificial neural networks, differential flatness and sliding modes are strategically integrated in the presented adaptive neural network control design approach. The robustness and efficiency of the magnetic suspension control system on desired smooth position reference profile tracking can be improved in this fashion. A single levitation control parameter is tuned on-line from a neural adaptive perspective by using information of the reference trajectory tracking error signal only. The sliding mode discontinuous control action is approximated by a neural network-based adaptive continuous control function. Control design is firstly developed from theoretical modelling of the nonlinear physical system. Next, dependency on theoretical modelling of the nonlinear dynamic system is substantially reduced by integrating B-spline neural networks and sliding modes in the electromagnetic levitation control technique. On-line accurate estimation of uncertainty, unmeasured external disturbances and uncertain nonlinearities are conveniently evaded. The effective performance of the robust trajectory tracking levitation control approach is depicted for multiple simulation operating scenarios. The capability of active disturbance suppression is furthermore evidenced. The presented B-spline neural network trajectory tracking control design approach based on sliding modes and differential flatness can be extended to other controllable complex uncertain nonlinear dynamic systems where internal and external disturbances represent a relevant issue. Computer simulations and analytical results demonstrate the effective performance of the new adaptive neural control method.</jats:p>Scopus© Citations 4 1 4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Improved Interleaved Boost Converter with Reduced Inductors(2024-01-01); 37 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Power shaping control of DC–DC converters with constant power loads(2020); Scopus© Citations 7 1 13