Time bound online uncertainty estimation based adaptive control design for DC–DC buck converters with experimental validation. (March 2021)
- Record Type:
- Journal Article
- Title:
- Time bound online uncertainty estimation based adaptive control design for DC–DC buck converters with experimental validation. (March 2021)
- Main Title:
- Time bound online uncertainty estimation based adaptive control design for DC–DC buck converters with experimental validation
- Authors:
- Nizami, Tousif Khan
Chakravarty, Arghya
Mahanta, Chitralekha - Abstract:
- Abstract: In this paper, an adaptive controller is proposed for DC–DC buck converters featuring prescribed time bound estimation of unknown system uncertainties and exogenous disturbances followed by nominal output performance recovery. The objective of the proposed control is to attain a robust output voltage tracking in buck converter in presence of parametric, non-parametric, matched and mismatched perturbations across wide operating range. Different from neural network estimators and characterizing substantially low computational complexity, an online estimator is presented to reconstruct the incurred uncertainty. The estimated additive uncertainty is thereafter fed to the nominal backstepping controller for subsequent compensation in finite time. Exact recovery of nominal output voltage tracking is claimed in a piecewise sense owing to the accuracy and precise estimation of the unknown unparametrized lumped uncertainty manifested in the form of large sudden variations in load and input voltage. Rigorous performance and stability analysis of the online estimator, along with similar analysis of the overall tracking control system are undertaken. Extensive numerical study is carried out to investigate the performance of the proposed control scheme. Further, experimentation of the proposed controller on a dc–dc buck converter using control desk DS1103 with an embedded TMS320F240 processor has been performed. The obtained experimental results demonstrate a good agreementAbstract: In this paper, an adaptive controller is proposed for DC–DC buck converters featuring prescribed time bound estimation of unknown system uncertainties and exogenous disturbances followed by nominal output performance recovery. The objective of the proposed control is to attain a robust output voltage tracking in buck converter in presence of parametric, non-parametric, matched and mismatched perturbations across wide operating range. Different from neural network estimators and characterizing substantially low computational complexity, an online estimator is presented to reconstruct the incurred uncertainty. The estimated additive uncertainty is thereafter fed to the nominal backstepping controller for subsequent compensation in finite time. Exact recovery of nominal output voltage tracking is claimed in a piecewise sense owing to the accuracy and precise estimation of the unknown unparametrized lumped uncertainty manifested in the form of large sudden variations in load and input voltage. Rigorous performance and stability analysis of the online estimator, along with similar analysis of the overall tracking control system are undertaken. Extensive numerical study is carried out to investigate the performance of the proposed control scheme. Further, experimentation of the proposed controller on a dc–dc buck converter using control desk DS1103 with an embedded TMS320F240 processor has been performed. The obtained experimental results demonstrate a good agreement with the simulation findings. … (more)
- Is Part Of:
- IFAC journal of systems and control. Volume 15(2021)
- Journal:
- IFAC journal of systems and control
- Issue:
- Volume 15(2021)
- Issue Display:
- Volume 15, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 15
- Issue:
- 2021
- Issue Sort Value:
- 2021-0015-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-03
- Subjects:
- DC–DC converter -- uncertain systems -- adaptive control -- estimation -- finite time convergence -- mismatched uncertainty
Automatic control -- Periodicals
Relay control systems -- Periodicals
Embedded computer systems -- Periodicals
Feedback control systems -- Periodicals
Artificial intelligence -- Periodicals
Artificial intelligence
Automatic control
Embedded computer systems
Feedback control systems
Relay control systems
Electronic journals
Periodicals
629.89 - Journal URLs:
- https://www.sciencedirect.com/science/journal/24686018 ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.ifacsc.2020.100127 ↗
- Languages:
- English
- ISSNs:
- 2468-6018
- Deposit Type:
- Legaldeposit
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