Time bound online uncertainty estimation based adaptive control design for DC-DC buck converters with experimental validation

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 agreement with the simulation findings.