Neural network-based fixed-time sliding mode control for a class of nonlinear Euler-Lagrange systems

作者：

Highlights：

• The fixed-time tracking control problem is addressed for an EL system with exogenous disturbances and uncertain dynamics.

• A neural network-based adaptive estimation algorithm is employed to suppress the negative impacts of imperfections.

• Adaptive sliding mode techniques are proposed to ensure the EL system following the desired trajectory within a fixed-time.

• The superiority of the proposed control strategies is substantiated by simulation results of a robotic manipulator system.

摘要

•The fixed-time tracking control problem is addressed for an EL system with exogenous disturbances and uncertain dynamics.•A neural network-based adaptive estimation algorithm is employed to suppress the negative impacts of imperfections.•Adaptive sliding mode techniques are proposed to ensure the EL system following the desired trajectory within a fixed-time.•The superiority of the proposed control strategies is substantiated by simulation results of a robotic manipulator system.

论文关键词：Nonlinear Euler-Lagrange systems,Trajectory tracking control,Neural network-based adaptive control,Fixed-time control

论文评审过程：Received 6 July 2021, Revised 6 September 2021, Accepted 29 September 2021, Available online 17 October 2021, Version of Record 17 October 2021.

论文官网地址：https://doi.org/10.1016/j.amc.2021.126718