@inproceedings{192d543be7704be490ec64ff8d680ad9,
title = "Adaptive variable-gain sliding mode control of robot manipulators with full state constraints",
abstract = "This paper introduces an adaptive variable gain sliding mode control strategy tailored for robotic manipulators with uncertainties and system constraints. A novel sliding surface is initially crafted to handle state constraints, where the gain is adjusted based on the proximity of system states to predefined constraints. Subsequently, an adaptive sliding mode control (SMC) scheme is formulated to ensure tracking error convergence. A neural network (NN) is harnessed to estimate uncertainties, with a composite learning approach developed to expedite NN weight updates. The Lyapunov framework validates the closed-loop system's stability, and simulations underscore the efficacy of the proposed identification and control algorithms.",
keywords = "full state constraints control, neural networks, robotic manipulator, Variable gain sliding mode control",
author = "Yingqi Guo and Yufei Liu and Dongdong Zheng",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 14th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2025 ; Conference date: 09-05-2025 Through 11-05-2025",
year = "2025",
doi = "10.1109/DDCLS66240.2025.11065705",
language = "English",
series = "Proceedings of 2025 IEEE 14th Data Driven Control and Learning Systems Conference, DDCLS 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1299--1304",
editor = "Mingxuan Sun and Ronghu Chi",
booktitle = "Proceedings of 2025 IEEE 14th Data Driven Control and Learning Systems Conference, DDCLS 2025",
address = "United States",
}