Time-Varying BLFs-Based Adaptive Neural Network Finite-Time Command-Filtered Control for Nonlinear Systems

This article deals with the adaptive neural network (NN) finite-time (FT) command-filtered tracking control problem for a class of nonlinear systems with time-varying full-state constraints. Based on the asymmetric time-varying barrier Lyapunov functions (TVBLFs), the issue of time-varying full-state constraints is settled. The influence of unknown items in the system can be eliminated by the adaptive NN control method. Moreover, the improved FT command filter is introduced to relax the restriction on the input signal and solve the explosion of complexity (EOC) problem. Meanwhile, the FT error compensation mechanism is developed to eliminate the influence of filtering error. It is shown that the proposed strategy can guarantee FT boundedness of all the signals in the closed-loop system and FT convergence of the tracking error. An example verifies the effectiveness of the proposed control method.

Automated summary

This article investigates the problem of adaptive neural network finite-time command-filtered tracking control for a certain class of nonlinear systems with time-varying full-state constraints. The issue of time-varying full-state constraints is resolved using asymmetric time-varying barrier Lyapunov functions. The adaptive neural network control method eliminates the influence of unknown items in the system. Additionally, an improved finite-time command filter is introduced to relax the restrictions on the input signal and solve the explosion of complexity problem. Meanwhile, a finite-time error compensation mechanism is developed to eliminate the influence of filtering error. It is demonstrated that the proposed strategy guarantees the boundedness and convergence of signals in the closed-loop system, and the effectiveness of the control method is verified through an example.