Robust Control Architecture For Waste Heat Harvesting With Non-Inverting Buck-Boost Converter

Gandini, Dario; Sorbellini, Pierpaolo; Chiaberge, Marcello

doi:10.23919/epe21ecceeurope50061.2021.9570715

Cited by 1 publication

(2 citation statements)

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“…The conditions of ( 33), ( 34), (35), and ( 36) are aimed to improve the nominal good performance and stability, which means that the error e will be small even in the presence of input disturbances. The nominal model of non-inverting buck-boost converter G is augmented by The format of () Ws  is stated in (39). 𝑀 2 is desired minimum H  peak value.…”

Section: Robust Control Designmentioning

confidence: 99%

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Simultaneous Enhancement of Photovoltaic System Intermittency and Damping Load Variations in Noninverting Buck-Boost Converters Using Robust Weighted Mixed-Sensitivity Control

Fauzi Iskandar,

Hanadi,

Hartono

et al. 2024

IEEE Access

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Due to the intermittent and uncertainty nature of photovoltaic system, their incorporation within a DC microgrid presents a challenge to the primary control, which directly interfaces with the generation converter. Utilizing a weighted mix-sensitivity control, this study aims to amplify the robustness of a non-inverting buck-boost converter integrated photovoltaic system in addressing the uncertainties and disturbances arising from simultaneous fluctuations in irradiance, temperature, and load. The robust control algorithm was formulated by employing frequency-weighting functions and imposing a requirement for the minimum norm of the transformation matrix to accomplish a robust performance and robust stability. Furthermore, the involvement of reference models into a robust control synthesis offerred additional advantages in enhancing the damping of the system. Consequently, the entire design configuration could effectively establish the converter robustness against both generations and load intermittencies occurring simultaneously. The simulation and experiment result is demonstrated to illustrate the efficiency of the designed algorithm.

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Section: Robust Control Designmentioning

confidence: 99%

“…This approach addresses issues such as control burden and environmental variable interference. Gandini et al [39] investigated robust control architectures to address the issue of thermoelectric generator instability due to temperature fluctuations while simultaneously improving the efficiency of the non-inverting buck-boost converter.…”

Section: Introductionmentioning

confidence: 99%