Chebyshev Fuzzy Neural Sliding Mode Controller With Extended State Observer for DC–DC Buck Converter

To address the voltage regulation problem of DC-DC buck converter under parameter uncertainties and lumped disturbances, a first-order sliding mode control method is designed based on a self-evolving recurrent Chebyshev fuzzy neural network (SERCFNN) and a finite-time extended state observer (FTESO). A FTESO is constructed to estimate the lumped disturbances and then is incorporated into the first-order sliding mode controller via real-time feedforward compensation. Compared with the conventional extended state observer (ESO), the FTESO not only achieves higher estimation accuracy but also ensures finite-time convergence of the estimation error. To compensate for the adverse effects of system uncertainties, the SERCFNN is employed to estimate the nonlinear dynamics. This incorporates a structure self-learning mechanism, which dynamically generates fuzzy rules to attain a suitable network architecture. Comparative simulation and experimental results demonstrate that under various test conditions, the proposed hybrid method has superior dynamic performance and disturbance rejection capability.