Stable Convergence Control of the Buck Converter Based on Iterative Learning Method

To enhance the stability and robustness of the DC converter, a two-dimensional (2D) iterative learning control method suitable for the Buck converter is proposed. This control method is derived through a continuous-discrete model. Combining the error dynamics equation and the iterative learning method (ILM) described with Roesser theory, effective learning rules and sufficient conditions for convergence are obtained. By contrast with the iterative learning algorithm of the conventional Buck converter, this algorithm clarifies the learning gain, deeply learns the structure and parameters of the converter, and obtains an accurate learning dynamics model. Moreover, numerical simulations of the Buck converter with specific circuit parameters are also conducted. The simulation results demonstrate that the control learning rules are less restrictive with a swift transient response, which are robust without complex feedback compensation circuits. Thereby, it elucidates a new approach to the application of the Buck converter.