Learning-Based Fault Identification and Fault-Tolerant Control for Quadruped Robots With Small Samples

Quadruped robots may experience faults during task execution, and the scarcity of sufficient real fault data poses significant challenges for fault diagnosis and fault-tolerant control (FTC). Building on the foundations of fault detection and classification, this article proposes a data-driven fault identification and FTC framework with small-sample conditions. The proposed data generation and feature enhancement methods fully exploit the information within limited datasets, thereby enhancing fault identification accuracy and enabling effective FTC. Finally, the effectiveness of the proposed approach is validated through both simulation and a real quadruped robot platform.

• Publication year

  2026

• Venue

  IEEE transactions on industrial electronics (1982. Print)

• Publication date

  2026-03-01

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/TIE.2025.3618921
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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