ESN-Observer-Based Adaptive Stabilization Control for Delayed Nonlinear Systems with Unknown Control Gain

This paper investigates the observer-based adaptive stabilization control problem for a class of time-delay nonlinear systems with unknown gain using an echo state network (ESN). In order to handle functions, new recurrent neural (RNN) approximation method called ESN is utilized. It improves accuracy, reduces computing cost, and simple train. To address issue gain, Nussbaum function used, Lyapunov–Krasovskii functionals are used delay term. The backstepping strategy command filtering methodology then create controller. All closed-loop system’s signals predicted be confined by Lyapunov stability theory. Finally, simulation example demonstrate effectiveness suggested mechanism.