Adaptive Backstepping Terminal Sliding Mode Control of Nonlinear System Using Fuzzy Neural Structure

An adaptive backstepping terminal sliding mode control (ABTSMC) method based on a multiple−layer fuzzy neural network is proposed for class of nonlinear systems with parameter variations and external disturbances in this study. The utilized to estimate the function handle unknown uncertainties system reduce switching term gain. It has strong learning ability high approximation accuracy due combination recurrent network. parameters can be adaptively adjusted optimal values through laws derived from Lyapunov theorem. To stabilize signal, additional law by projection algorithm used coefficient. (TSMC) introduced basis control, which ensure that tracking error converges finite time. simulation example carried out DC–DC buck converter model verify effectiveness superiority method. contrasting results show ABTSMC−DHLRNN possesses higher steady−state faster transient response.