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Enhancing Visual Servoing Robustness: Integrating ISMC with Adaptive Neural Networks

EasyChair Preprint no. 13193

8 pagesDate: May 6, 2024


Visual servoing systems play a crucial role in robotics by enabling precise control of manipulators based on visual feedback. However, these systems often face challenges such as uncertainties, disturbances, and changes in environmental conditions. In this paper, we propose a novel approach to enhance the robustness of visual servoing systems by integrating Integral Sliding Mode Control (ISMC) with Adaptive Neural Networks (ANN). By combining the robustness of ISMC with the adaptability of ANN, the integrated framework aims to address the limitations of traditional control methods and improve performance in dynamic and uncertain environments. Through comprehensive simulations and experimental validations, we demonstrate the effectiveness of the proposed approach in achieving precise and reliable control in various visual servoing tasks.

Keyphrases: adaptive neural networks, Dynamic Environments, Integral sliding mode control, Robotics, robustness, Uncertainties, visual servoing

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Kailash Pandey and Wahaj Ahmed},
  title = {Enhancing Visual Servoing Robustness: Integrating ISMC with Adaptive Neural Networks},
  howpublished = {EasyChair Preprint no. 13193},

  year = {EasyChair, 2024}}
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