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Cited by 9Adaptive Control of Nonlinear SystemsRobotic Mechanisms and DynamicsDynamics and Control of Mechanical Systems

Adaptive Fault-Tolerant Tracking Control for Multi-Joint Robot Manipulators via Neural Network-Based Synchronization

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Le, Quang Dan, Yang, Erfu·2024·Sensors
DOI 10.3390/s24216837PMC PMC11548151
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Abstract

In this paper, adaptive fault-tolerant control for multi-joint robot manipulators is proposed through the combination of synchronous techniques and neural networks. By using a synchronization technique, the position error at each joint simultaneously approaches zero during convergence due to the constraints imposed by the synchronization controller. This aspect is particularly important in fault-tolerant control, as it enables the robot to rapidly and effectively reduce the impact of faults, ensuring the performance of the robot when faults occur. Additionally, the neural network technique is used to compensate for uncertainty, disturbances, and faults in the system via online updating. Firstly, novel robust synchronous control for a robot manipulator based on terminal sliding mode control is presented. Subsequently, a combination of the novel synchronous control and neural network is proposed to enhance the fault tolerance of the robot manipulator. Finally, simulation results for a 3-DOF robot manipulator are presented to demonstrate the effectiveness of the proposed controller in comparison to traditional control techniques.

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  • Neural-adaptive systems achieve high accuracy under actuator faults, demonstrated in multi-joint tracking with up to 94% accuracy.

    “leverage neural networks with architectures detailed in Section Key Technical Methods in Hybrid FTC to achieve high accuracy under actuator faults, as demonstrated in multi-joint tracking with up to 94% accuracy [11].”

    From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [11]

    Section 4.2 (page unavailable) reports that the neural-network controller outperformed comparison controllers in simulated multi-joint tracking with actuator faults. It reports tracking errors in radians, not accuracy as a percentage; the claimed 94% accuracy is unsupported. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Healthcare leverages neural synchronization for reliable surgical outcomes.

    “and healthcare leverages neural synchronization for reliable surgical outcomes [11,13].”

    From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [11]

    The Introduction describes a controller combining neural networks and synchronization for fault-tolerant robot manipulators, and Section 4.2 reports improved performance in simulation. The paper does not establish that healthcare uses this approach or that it produces reliable surgical outcomes; Section 5 says its practical viability in real-world robotic systems remains to be verified. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Neural-adaptive control utilizing a 128-node recurrent neural network delivers rapid responses in surgery and UAV swarms and achieves high accuracy in construction human-robot collaboration, despite significant computational demands.

    “Neural-adaptive control, utilizing a 128-node RNN, delivers rapid responses in surgery [11] and UAV swarms [27], achieving high accuracy in construction HRC [40], despite significant computational demands [10].”

    From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [11]

  • Digital Twins operating at a 10 Hz simulation rate reduce testing cycles and improve human-robot collaboration planning accuracy, despite minor synchronization lags.

    “Digital Twins, operating at a 10 Hz simulation rate, reduce testing cycles and improve HRC planning accuracy [45], despite minor synchronization lags [11].”

    From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [11]

    The citation to this source, [11], is attached to “despite minor synchronization lags,” not to the preceding claims cited to [45]. Section 4.2 reports delayed feedback from a fault estimator in a robot-controller simulation, but does not address synchronization lags in a Digital Twin. Searches for Digital Twins, a 10 Hz rate, and synchronization latency found no passage supporting that qualifier. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 49

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Authors
Le, Q. D., & Yang, E.
Affiliations
University of Strathclyde (GB)
Corresponding author
Yang, Erfu
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2024-10-24
Volume
24
Issue
21
Pages
6837
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s24216837
PMC
PMC11548151
OpenAlex
W4403731532
Cited by
9
References
50
Citations by year
2026: 4 · 2025: 5
FWCI
1.41
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
UK Net Zero Technology Centre (1716541)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
  • Physical Sciences › Engineering › Control and Systems Engineering › Robotic Mechanisms and Dynamics
  • Physical Sciences › Engineering › Control and Systems Engineering › Dynamics and Control of Mechanical Systems
Keywords
fault-tolerant control, synchronization control, terminal sliding mode control, neural network compensation, adaptive control, online parameter updating, trajectory tracking control

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