Self-Organizing Fuzzy Neural Nonsingular Fast 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 achieve voltage tracking control of a DC-DC buck converter. The NFTSMC can ensure the finite-time convergence property of the tracking error and avoid the singularity problem provided by the conventional TSMC. To compensate and alleviate the adverse effect of the system uncertainty, the SOCFNN is utilized to estimate the nonlinear dynamics of the converter system, in which a novel structure learning mechanism is constructed. The behavior behind this mechanism is that the number of the fuzzy rules can be dynamically generated and eliminated to obtain an appropriate network structure, and the learning performance of neural network is improved by introducing the Chebyshev expansions. In addition, all the parameter updating algorithms are obtained through the Lyapunov theorem, thus the neural network output can be adaptively adjusted to the optimal value. Both the simulation and experimental comparisons illustrate that the proposed controller presents higher voltage tracking accuracy and faster dynamic response under different test conditions.