Robotic manipulators are indispensable in various industrial applications, yet their performance is often compromised by parametric uncertainties, including variations in load, friction, and external disturbances. To overcome these challenges, this paper introduces a robust adaptive strategy with fractional differentiation’s for sliding mode control (AFO-SMC). While traditional sliding mode control (SMC) is renowned for its robustness, it suffers from chattering issues that degrade system efficiency. Integrating fractional-order calculus with SMC enhances control flexibility and robustness under uncertain conditions. The adaptive mechanism within the AFO-SMC dynamically adjusts control parameters, ensuring consistent tracking performance and stability despite significant variations on system parameters with external disturbances. The reliability of the proposed control scheme is validated through a comprehensive stability analysis, which is divided into two main parts: the first part ensures that the system state reaches and remains on the sliding surface, while the second stability analysis study focuses on eliminating the trajectory tracking error of the robotic arm. Simulation results demonstrate that AFO-SMC significantly outperforms conventional control methods, achieving high precision and robustness in the presence of uncertainties.
Khlif et al. (Fri,) studied this question.