Adaptive neural control of state delayed non-linear systems with unmodelled dynamics and distributed time-varying delays

In this study, a robust adaptive control is proposed for a class of strict-feedback state delayed non-linear systems with unmodelled dynamics and distributed time-varying delays using radial basis function neural networks. Dynamic uncertainties are dealt with using separation technique and introducing a dynamic signal. The terms including state time-varying delay and distributed time-varying delay uncertainties are compensated for by constructing appropriate Lyapunov-Krasovskii functionals. Using Young´s inequality, only one learning parameter need to be tuned online at each step of recursion. It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness of all signals in the closed-loop system. Simulation results demonstrate the effectiveness of the proposed approach.