Author
Listed:
- Radosław Nalepa
(Department of Electrical Power Engineering, Faculty of Electrical Engineering, Wrocław University of Science and Technology, PL50370 Wrocław, Poland)
- Karol Najdek
(Department of Electrical Power Engineering, Faculty of Electrical Engineering, Wrocław University of Science and Technology, PL50370 Wrocław, Poland)
- Błażej Strong
(Department of Electrical Power Engineering, Faculty of Electrical Engineering, Wrocław University of Science and Technology, PL50370 Wrocław, Poland)
Abstract
In this paper the D-decomposition technique is investigated as a source of non-linear boundaries used with the Genetic Algorithm (GA) search of a PI voltage compensator gains of the boost converter operating in Continuous Conduction Mode (CCM). The well known and appreciated boost converter has been chosen as a test object due to its right-half plane zero in the control-to-output ( c 2 o ) voltage transfer function. The D-decomposition, as a technique relying on the frequency sweeping, clearly indicates not only the global stability but, in its extended version, regions satisfying the required gain (GM) and phase (PM) margins. Such results are in form of easy to interpret functions K I = f ( K P ) . The functions are easy to convert to the GA constraints. The GA search, with three different performance indexes as the fitness functions, is applied to a control structure with time delays basing on identified c 2 o voltage transfer functions. The identification took place in an experiment and in simulation. Outcomes of the identification are compared to mathematically derived formula taking into account certain parasitics. A complete set of practically useful mathematical formulas together with their validation in simulation and experiment is included.
Suggested Citation
Radosław Nalepa & Karol Najdek & Błażej Strong, 2020.
"Hybrid Tuning of a Boost Converter PI Voltage Compensator by Means of the Genetic Algorithm and the D-Decomposition,"
Energies, MDPI, vol. 14(1), pages 1-20, December.
Handle:
RePEc:gam:jeners:v:14:y:2020:i:1:p:173-:d:473061
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