Adaptive Backstepping Control for DC-DC Buck Converters using Chebyshev Neural Network

This paper proposes a novel control technique for the Buck type DC-DC converters using adaptive backstepping control and Chebyshev neural network. To enhance the transient performance of both the capacitor voltage and the inductor current under nominal conditions, input voltage fluctuations and load variations, this control algorithm has been proposed. The systematic design of backstepping controller has been improvised by incorporating the approximation of unknown load resistance parameter by a single layer Chebyshev neural network. Results have been compared with a recently developed adaptive terminal sliding mode control technique. The proposed method significantly improves voltage and current transient performances.