Adaptive Neural Control for a Class of Strict-Feedback Nonlinear Systems With State Time Delays

This brief proposes a simple control approach for a class of uncertain nonlinear systems with unknown time delays in strict-feedback form. That is, the dynamic surface control technique, which can solve the "explosion of complexity" problem in the backstepping design procedure, is extended to nonlinear systems with unknown time delays. The unknown time-delay effects are removed by using appropriate Lyapunov-Krasovsii functionals, and the uncertain nonlinear terms generated by this procedure as well as model uncertainties are approximated by the function approximation technique using neural networks. In addition, the bounds of external disturbances are estimated by the adaptive technique. From the Lyapunov stability theorem, we prove that all signals in the closed-loop system are semiglobally uniformly bounded. Finally, we present simulation results to validate the effectiveness of the proposed approach.