Integrating Integral Sliding Mode Control into Adaptive Neural Network-Based Visual Servoing

EasyChair Preprint no. 13197

Abstract

Visual servoing is a critical component in robotic systems, enabling precise control of manipulators based on visual feedback. Traditional control methods often struggle with uncertainties and disturbances inherent in real-world environments. This research paper proposes a novel approach that integrates integral sliding mode control (ISMC) into adaptive neural network-based visual servoing (ANN-VS) to enhance robustness and accuracy. The synergy between ISMC's robustness and ANN-VS's adaptability is leveraged to address challenges such as modeling inaccuracies, external disturbances, and changes in environmental conditions. The proposed framework is evaluated through simulations and experiments, demonstrating its effectiveness in real-world scenarios.

Keyphrases: adaptive neural networks, Integral sliding mode control, machine learning, robotic control, robust control, visual servoing

@Booklet{EasyChair:13197,
  author = {Hiromi Morita and Wahaj Ahmed},
  title = {Integrating Integral Sliding Mode Control into Adaptive Neural Network-Based Visual Servoing},
  howpublished = {EasyChair Preprint no. 13197},

  year = {EasyChair, 2024}}