Concurrent Learning-Based Fixed-Time Sliding Mode Control for Space Manipulators With Uncertain Dynamics

Abstract

In space tasks such as deep space exploration and on-orbit service, space manipulators need to perform precise operations in complex environments such as microgravity and extreme temperature variations, and the dynamics of space manipulators show significant uncertainties due to environmental effects. To address these challenges, a finite-time saturation (FTsat) function enhanced concurrent learning-based fixed-time sliding mode control (FTsat-CL-FxTSMC) scheme is developed for trajectory tracking problem of space manipulators. First, an FTsat function is proposed, which ensures finite-time stability and eliminates the chattering phenomenon in sliding mode control, thereby reducing the sensitivity to boundary layer selection and enhancing tracking accuracy. Building on this improvement, the proposed FTsat-CL-FxTSMC framework integrates the FTsat function with an adaptive parameter estimator, ensuring fixed-time convergence of tracking errors while accurately compensating for dynamic uncertainties. The effectiveness of proposed method is verified through qualitative and quantitative analysis of simulation results.