Data-driven stabilizing control of DC–DC converters with unknown active loads. (February 2020) - Ruiz-Martinez, O.F.; Mayo-Maldonado, J.C.; Escobar, G.; Valdez-Resendiz, J.E.; Maupong, T.M.; Rosas-Caro, J.C.
Abstract: We address the issue of stabilizing control design of DC–DC power converters with active loads, e.g. distributed generation systems, cascade converters, converters feeding DC buses, etc. We test the results in the presence of potential destabilizers, such as constant power loads (CPLs). The proposed approach is particularly devised for those situations where a full network dynamic model is either not available or it has an unsuitable level of complexity—in terms of number of state–space variables and equations, e.g. multiple loads, DC distribution networks, micro grids, etc. This is due to its model-free approach based on a data-driven strategy, which is able to guarantee stability using a LMI-Lyapunov approach. Elements of behavioral system theory such as linear difference systems, quadratic difference forms and Lyapunov theory, are crucial for the development of the proposed stabilizing control schemes. We also provide simulation and experimental results to corroborate the practical advantages of the proposed framework. Highlights: A data-driven (model-free) approach to control DC–DC converters is introduced. The issue of instability induced by active constant power loads is addressed. Stability is guaranteed via a Lyapunov approach. Behavioral system theory is the pivotal figure for the analysis. Experimental results using a boost converter feeding a constant power load are presented.