Gain-analytical equations generalized for FOPID controllers — An application with DC–DC power converters

This study presents algebraic formulations for determining the gains of a fractional-order PID (FOPID) regulator, with attention to gain crossover frequency, phase margin, and robustness against gain variations (iso-damping). The differential evolution (DE) algorithm is employed for optimal tuning of fractional-order values, aligned with FOPID gain parameters. FPGA-in-the-loop simulations for Buck and DC–DC Boost converters are conducted to validate the DE-tuned FOPID controllers, optimized using the L2 norm and compared to MATLAB-Simulink simulations. The approach introduces a key innovation: disaggregating the FOPID equation for memory efficiency. Simulations and experiments demonstrate the controller’s precision in setpoint tracking and resilience to load and input voltage variations in Buck and DC–DC Boost converters, with effective damping of load disturbances. A test bench validated the proposed method, which was also compared to conventional PID, Type 3, and PIDD2-IMC controllers. While the proposed FOPID controller performed better in the presented results, a conclusive comparison could not be drawn due to unequal tuning efforts. Future work will focus on a more thorough comparison of controllers.

Authors

Publication Details

Journal
e-Prime - Advances in Electrical Engineering Electronics and Energy
Published
2025-03-20
DOI
https://doi.org/10.1016/j.prime.2025.100967
Primary Topic
Advanced Control Systems Design
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Gain-analytical equations generalized for FOPID controllers — An application with DC–DC power converters

Leon M. Tolbert, João Onofre Pereira Pinto, Anderson S. Volpato, Ruben Barros Godoy et al.
e-Prime - Advances in Electrical Engineering Electronics and Energy
Advanced Control Systems Design
article

Gain-analytical equations generalized for FOPID controllers — An application with DC–DC power converters

Leon M. Tolbert, João Onofre Pereira Pinto, Anderson S. Volpato, Ruben Barros Godoy, Edson Antonio Batista, Luís Felipe da S.C. Pereira, Moacyr A. G. de Brito
article en

Abstract

This study presents algebraic formulations for determining the gains of a fractional-order PID (FOPID) regulator, with attention to gain crossover frequency, phase margin, and robustness against gain variations (iso-damping). The differential evolution (DE) algorithm is employed for optimal tuning of fractional-order values, aligned with FOPID gain parameters. FPGA-in-the-loop simulations for Buck and DC–DC Boost converters are conducted to validate the DE-tuned FOPID controllers, optimized using the L2 norm and compared to MATLAB-Simulink simulations. The approach introduces a key innovation: disaggregating the FOPID equation for memory efficiency. Simulations and experiments demonstrate the controller’s precision in setpoint tracking and resilience to load and input voltage variations in Buck and DC–DC Boost converters, with effective damping of load disturbances. A test bench validated the proposed method, which was also compared to conventional PID, Type 3, and PIDD2-IMC controllers. While the proposed FOPID controller performed better in the presented results, a conclusive comparison could not be drawn due to unequal tuning efforts. Future work will focus on a more thorough comparison of controllers.

e-Prime - Advances in Electrical Engineering Electronics and EnergyVol. 12
Affordable and clean energy
Openalex Percentile: Top 95%
Advanced Control Systems Design
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.