Neural Network Based Fixed-time Sliding Mode Control for Robotic Systems with Disturbances
Year: 2026
Type: article
Abstract: This article addresses the trajectory tracking control problem of a robotic systems under parameter uncertainties and external disturbances. First, the fixed-time stability of a second-order system is established, and based on this, an integral sliding variable is introduced to ensure fixed-time stability while avoiding singularity issue. To enhance tracking performance, an adaptive neural network is designed to estimate the uncertainties and external disturbances. Consequently, a neural network-based nonsingular fixed-time sliding mode control (FTSMC) is proposed. The fixed-time stability of the system under the proposed control scheme is proved based on homogeneity and Lyapunov stability theory. Finally, the effectiveness and superiority of the proposed method are verified by numerical simulation and comparative experiments on Franka Emika Panda robotic platform.
Institution: Harbin Institute of Technology
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,645
OpenAlex ID: W7225404993