Singularity-Free Fixed-Time Neuro-Adaptive Control for Robot Manipulators in the Presence of Input Saturation and External Disturbances

Abstract

This article proposes a singularity-free fixed-time neuro-adaptive control strategy for robot manipulators, with the goal of addressing trajectory-tracking challenges presented by model uncertainties, external disturbances, and input saturation. To mitigate the impact of input saturation, an auxiliary system is introduced. Combining the backstepping technique, a fixed-time neuro-adaptive controller is designed to ensure that tracking errors converge within a small region around the origin within a fixed time, with the upper bound of convergence time being independent of initial conditions. Notably, the direct avoidance of singularity is achieved by constructing quadratic-fraction functions in both the virtual controller and the actual controller, eliminating the need for filters or piecewise continuous functions. This simplifies and streamlines the stability analysis process. To validate the effectiveness of this strategy, numerical simulations are conducted.

Keywords

• Robot manipulators
• adaptive control
• fixed-time convergence
• neural network
• input saturation