Fuzzy Neural Nonsingular Terminal Sliding Mode Control of DC-DC Buck Converter

In this paper, a nonsingular fast terminal sliding mode control (NFTSMC) with a self-organizing Chebyshev fuzzy neural network (SOCFNN) is designed to obtain voltage tracking control of a DC-DC buck converter. The NFTSMC can ensure the finite-time convergence property of the voltage tracking error and avoid the singularity problem. To compensate and alleviate the effects of the system uncertainty, the SOCFNN with structure learning mechanism is utilized to estimate the nonlinear function in the system. Experimental comparisons illustrate that the proposed control method presents higher voltage tracking accuracy and faster dynamic response.