Abstract
This article proposes a novel Zernike radial neural network based adaptive control architecture for the closed-loop control of output DC voltage in DC-DC buck power converter. The proposed combination of novel Zernike radial neural network estimator and the adaptive backstepping controller effectively compensates for wide range of perturbations affecting the converter system, in an online manner. The closed loop stability of the DC-DC buck power converter with the proposed neuro-adaptive backstepping controller is proven by Lyapunov stability criterion. Numerical simulations are conducted to examine the effectiveness of the proposed controller under start-up response and step changes in the load, source voltage and reference output voltage. Furthermore, the simulation findings are validated by conducting extensive real-time investigation on a laboratory prototype, under a wide range of operating points. The results obtained shows a significant improvement in the transient response of both output voltage and inductor current of the converter, relative to the relevant control methods proposed in the recent past.
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