Identification and local linear control of a DC-DC buck converter using local model networks

Abstract

This paper proposes the idea of a local linear controller for voltage regulation of DC-DC buck converters. The control strategy is based on the data-driven identification of the converter model through the local model networks (LMNs) and use of the identified model for local linear control (LLC) of the voltage controlled buck converter. The LMN is compose of the local linear models (LLMs) and models the nonlinear dynamics of a converter by dividing its operating regime into several sub-regimes and then fitting an LLM on each sub-regime. The identified model of the converter is used for the implementation of a LLC with the objective of regulating the converter output voltage when the input voltage and the load power absorption vary in time. In the proposed local linear control scheme, the produced input control is equivalent to the weighted average of the control inputs of all local linear models. The proposed LLC scheme is employed for identification of the dynamics of a buck converter also in condition of noisy measurement data. Simulation results obtained by Matlab/Simulink demonstrate satisfactory performance of the proposed LLC scheme, much better than a conventional PI controller.