Fuzzy Neural Network Sliding Mode Controller for DC-DC Buck Converter

To promote the robust ability and voltage tracking performance of DC-DC buck converter, a voltage tracking control system using a neural network estimator is proposed. The proposed control system incorporates a super-twisting sliding-mode controller (STSMC) and a recurrent Chebyshev fuzzy neural network using a self-evolving mechanism, where the STSMC can guarantee the output voltage tracking error converges to zero, and a self-evolving recurrent Chebyshev fuzzy neural (SERCFNN) is developed to estimate the nonlinear functional certainty of DC-DC buck converter system. The SERCFNN combines the advantages of the self-evolving recurrent fuzzy neural network (SERFNN) and Chebyshev function network (CFN). The super twisting algorithm has strong robustness and can suppress the chattering of sliding-mode control. SERCFNN is combined with STSMC to estimate the nonlinear function. Meanwhile, SERCFNN estimates the unknown function by using the actual system variable, which can further suppress the chattering of the system to a certain extent and make the output voltage of DC-DC buck converter more accurate. The effectiveness and superiority of the performance are exhibited with simulation and experimental studies and comprehensive comparisons.