Neural network–based adaptive fractional-order terminal sliding mode control

This paper proposes an adaptive fractional-order (FO) terminal sliding mode control (TSMC) scheme to the robust current control of active power filter (APF) using a recurrent meta-cognitive fuzzy neural network (RMCFNN). An FO TSMC is developed by considering that the parametric perturbations and the external disturbances of APF are bounded. Compared with conventional TSMC approach, the proposed scheme, with an FO sliding surface, can obtain enhanced finite-time high-precision tracking performance due to another degree of freedom. Then, a novel observer-based FO TSMC is derived to achieve an absorbing model-free feature arising from RMCFNN. To improve the capabilities in managing the uncertainties, the specific online updating schemes for the structure and parameters of RMCFNN are designed. Meanwhile, closed-loop stability and finite-time convergence characteristic can be achieved using Lyapunov theory. Finally, simulation and experimental results indicate that the proposed observer-based FO TSMC can be easily implemented by microcontroller and has superior control performance compared with other existing schemes.