Adaptive neural network control of DC–DC power converter

This article proposes a novel Zernike radial neural network based adaptive control architecture for 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 shown using 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 show 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. •Zernike radial neural network (ZRNN)-adaptive control is proposed for output voltage regulation in dc-dc buck converter.•ZRNN estimates the unknown load disturbance online and feeds it to the controller for subsequent compensation.•A promising dynamic performance, rapid estimation and stability are ensured concurrently with the proposed controller.•Extensive performance comparison is provided to prove the merit of proposed methodology.•Simulation & experimental results demonstrate efficacy of the proposed controller and its immense potential for technology transfer to real-time buck converter applications.