Adaptive Dynamic Surface Control of Buck Converter Based on Energy Storage System

Abstract

To enhance the stability of the Buck converter and the capacity of resisting disturbance of the controller and converter, come up with an adaptive dynamic surface control strategy based on voltage mode control, which improves the ability of the energy storage system to stabilize the output voltage when the input voltage or load changes. The Radial Basis Function (RBF) neural network is combined with the finite covering lemma to approximate the unknown nonlinear functions with time-delay in the system. The error conversion and performance functions are adopted to ensure the tracking performance index. The adaptive dynamic surface controller is applied to ensure the stability of the output voltage of the system when the external conditions change. The proposed control method is tested on the hardware-in-the-loop (HIL) experimental platform. The consequence illustrate that the designed controller is feasible and has good voltage stability performance.