Finite-Time Composite Learning Control of Strict-Feedback Nonlinear System Using Historical Stack

This article investigates the finite-time control of the strict-feedback nonlinear system using composite learning based on the historical stack. The controller design adopts the backstepping scheme while the nonlinear function is introduced to avoid the singularity problem. The first-order Levant differentiator is introduced to obtain the filtered command signal and the compensation signal is further constructed. To indicate the learning performance, the historical data over the moving time window are analyzed to construct the predictor error using the maximum-minimum singular value algorithm. Furthermore, the finite-time neural update law is proposed. The stability of the closed-loop system is analyzed via the Lyapunov approach. The performance of the proposed method is verified using simulations.