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An efficient sliding mode observer-based model predictive control: experimental implementation on a DC/DC power converter

In this paper, a discretization-based sliding mode observer (DBSMO) is proposed for the state and parameter estimation of nonlinear systems. In the DBSMO structure, an accurate discretized dynamics are derived for the state and parameter update of the sliding mode observer (SMO) instead of integration-based state update. In this way, faster converging observer dynamics are obtained. The stability properties of the conventional SMO remain the same for DBSMO. In the application presented here, first a real-time DC/DC power converter is designed with a computer interface. Then, to show the enhancement of convergence properties, conventional SMO and DBSMO-based model predictive controllers are designed and applied to control of the DC/DC power converter. Through simulation and experimental results, it is shown that the estimation performance of SMO is greatly improved so that the tracking performance is also increased, which are the main contributions of the paper.