Adaptive fault-tolerant control for uncalibrated camera–robot system with multiple uncertainties
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Reinforcement learning with a 0.1 learning rate stabilizes systems in harvesting and minimizes construction errors, though exploration risks persist.
“Reinforcement learning with a 0.1 learning rate stabilizes systems in harvesting [28] and minimizes construction errors [34], though exploration risks persist [13].”
From Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability (2025) · cites this paper as [28]
Cites
On the Adaptive Control of Robot Manipulators
Slotine, J.-J. E., & Li, W. (1987) · The International Journal of Robotics Research · doi:10.1177/027836498700600303 · Cited by 2,288 · see citing works
Wang, W., & Wen, C. (2010) · Automatica · doi:10.1016/j.automatica.2010.09.006 · Cited by 671 · see citing works
Tang, X., Tao, G., & Joshi, S. M. (2007) · Automatica · doi:10.1016/j.automatica.2007.03.019 · Cited by 404 · see citing works
Adaptive Fault-Tolerant Attitude Tracking Control of Spacecraft With Prescribed Performance
Hu, Q., Shao, X., & Guo, L. (2017) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2017.2775626 · Cited by 397 · see citing works
Uncalibrated visual servoing of robots using a depth-independent interaction matrix
Liu, Y., Wang, H., Wang, C., & Lam, K. (2006) · IEEE Transactions on Robotics · doi:10.1109/tro.2006.878788 · Cited by 311 · see citing works
Self-citationAn adaptive actuator failure compensation controller using output feedback
Tao, G., Chen, S., & Joshi, S. M. (2002) · IEEE Transactions on Automatic Control · doi:10.1109/9.989150 · Cited by 290 · see citing works
Neural PID Control of Robot Manipulators With Application to an Upper Limb Exoskeleton
Yu, W., & Rosén, J. (2012) · IEEE Transactions on Cybernetics · doi:10.1109/tsmcb.2012.2214381 · Cited by 186 · see citing works
Yang, H., Jiang, Y., & Yin, S. (2020) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2020.2965884 · Cited by 165 · see citing works
Van, M., Phu, D. X., & Mavrovouniotis, M. (2019) · ISA Transactions · doi:10.1016/j.isatra.2019.06.017 · Cited by 160 · see citing works
Mao, Z., Yan, X., Jiang, B., & Chen, M. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2918543 · Cited by 142 · see citing works
Zeghlache, S., Mekki, H., Bouguerra, A., & Djerioui, A. (2018) · ISA Transactions · doi:10.1016/j.isatra.2018.06.003 · Cited by 124 · see citing works
Robust fault tolerant control of robot manipulators with global fixed-time convergence
Van, M., & Ceglarek, D. J. (2020) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2020.11.002 · Cited by 109 · see citing works
Adaptive Image-Based Visual Servoing With Temporary Loss of the Visual Signal
Shi, H., Sun, G., Wang, Y., & Hwang, K. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2865004 · Cited by 92 · see citing works
Robust fault detection based on adaptive threshold generation using interval LPV observers
de, S. M., Puig, V., & Blesa, J. (2011) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.1263 · Cited by 70 · see citing works
Hu, J., Zhang, X., Zhang, D., Chen, Y., Ni, H., & Liang, H. (2023) · ISA Transactions · doi:10.1016/j.isatra.2023.10.028 · Cited by 67 · see citing works
Self-citationRobust Fault-Tolerant Formation Control for Tail-Sitters in Aggressive Flight Mode Transitions
Liu, D., Liu, H., Lewis, F. L., & Wan, Y. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2914478 · Cited by 62 · see citing works
Fault detection and diagnosis with a novel source-aware autoencoder and deep residual neural network
Amini, N., & Zhu, Q. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.067 · Cited by 59 · see citing works
Sensor Fault Detection and Diagnosis for an Unmanned Quadrotor Helicopter
Zhong, Y., Zhang, W., Zhang, Y. M., Zuo, J., & Zhan, H. (2019) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-019-01002-4 · Cited by 44 · see citing works
Visual Servoing of Flying Robot Based on Fuzzy Adaptive Linear Active Disturbance Rejection Control
Sun, C., Liu, C., Feng, X., & Jiao, X. (2021) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2021.3053083 · Cited by 40 · see citing works
A new image-based visual servoing method with velocity direction control
Dong, J., & Zhang, J. (2020) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2020.01.012 · Cited by 36 · see citing works
A novel neural second-order sliding mode observer for robust fault diagnosis in robot manipulators
Van, M., Kang, H., & Suh, Y.-S. (2013) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-013-0055-5 · Cited by 36 · see citing works
Robotic manipulation based on 3-D visual servoing and deep neural networks
Al-Shanoon, A. A. S., & Lang, H. (2022) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2022.104041 · Cited by 34 · see citing works
Dynamic learning-based fault tolerant control for robotic manipulators with actuator faults
Zhang, F., Wu, W., Song, R., & Wang, C. (2022) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2022.11.044 · Cited by 33 · see citing works
Self-citationQiu, Z., Hu, S., & Liang, X. (2020) · ISA Transactions · doi:10.1016/j.isatra.2020.06.013 · Cited by 29 · see citing works
Multiple model-based fault-tolerant control system for distributed drive electric vehicle
Liu, L., Shi, K., Yuan, X., & Li, Q. (2019) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-019-2047-6 · Cited by 28 · see citing works
Logarithmic Observation of Feature Depth for Image-Based Visual Servoing
Li, X., Zhao, H., & Ding, H. (2021) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2021.3125698 · Cited by 25 · see citing works
Briot, S., Martinet, P., & Rosenzveig, V. (2015) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2489499 · Cited by 25 · see citing works
Song, J.-G., & Zhang, J. (2023) · ISA Transactions · doi:10.1016/j.isatra.2023.11.010 · Cited by 24 · see citing works
Kinematics-based incremental visual servo for robotic capture of non-cooperative target
Dong, G., & Zhu, Z. H. (2018) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2018.10.011 · Cited by 22 · see citing works
Robust adaptive visual tracking control for uncertain robotic systems with unknown dead-zone inputs
Wang, F., Liu, Z., Chen, C. L. P., & Zhang, Y. (2019) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2019.05.040 · Cited by 17 · see citing works
Visual tracking control for an uncalibrated robot system with unknown camera parameters
Hua, C., Wang, Y., & Guan, X. (2013) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2013.06.002 · Cited by 15 · see citing works
Robust homography-based visual servo control for a quadrotor UAV tracking a moving target
Huang, Y., Zhu, M., Chen, T., & Zheng, Z. (2022) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2022.12.036 · Cited by 15 · see citing works
Visual-RRT: Integrating IBVS as a steering method in an RRT planner
Reyes, R., Becerra, I., Murrieta-Cid, R., & Hutchinson, S. (2023) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2023.104525 · Cited by 12 · see citing works
Boundary event-triggered FTC of uncertain Euler–Bernoulli beam systems with actuator failures
Zhang, X.-Y., Li, Y., & Hou, Z. (2023) · Aerospace Science and Technology · doi:10.1016/j.ast.2023.108823 · Cited by 9 · see citing works
Adaptive Compensation of Actuator Failures using Multiple Models
Chakravarty, A., Nizami, T. K., Kar, I., & Mahanta, C. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.1681 · Cited by 9 · see citing works
Fault diagnosis and fault tolerant control of mobile robot based on neural networks
Li, Z. (2009) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2009.5212376 · Cited by 8 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Hybrid Fault-Tolerant Control in Cooperative Robotics: Advances in Resilience and Scalability
Urrea, C. (2025) · Actuators · doi:10.3390/act14040177 · Cited by 19 · see citing works
“…reduction [30], 85% uptime [14] Scalability limits [39] Neural Adaptive 94% accuracy [11] High GPU needs [27] Reinforcement Learning 90% recovery [28], 98% resilience [17] Safety risks [13] Hybrid AI 95% efficiency [41], 98% resilience [8] 20–30% overhead [42] Hybrid redundancy combines…”
InfluentialUncalibrated Visual Servo Control Using Adaptive Kalman Filtering for Robots Driven by DC Motors
Mou, X., Yu, H., Guo, Q., Yang, Q., & Meng, X. (2025) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc65474.2025.11090592 · Cited by 1 · see citing works
Shu, D., Yang, L., Chen, Y., & Lai, G. (2026) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2026.108639
Self-citation
- Authors
- Wang, C., Yang, L., Chen, Y., & Lai, G.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Guangdong University of Technology (CN)
- Corresponding author
- Yang, Liang
- Venue
- Journal of the Franklin Institute
- Publisher
- Elsevier BV
- Published
- 2024-09
- Volume
- 361
- Issue
- 14
- Pages
- 107048
- ISSN
- 0016-0032 (ISSN-L)
- OpenAlex
- W4400106041
- Cited by
- 3
- References
- 36
- Citations by year
- 2026: 1 · 2025: 2
- FWCI
- 0.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the Franklin Institute (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Control and Dynamics of Mobile Robots
- Keywords
- adaptive fault-tolerant control, uncalibrated cameras, visual servoing, robot manipulators, actuator faults, loss of efficacy, fault estimation, adaptive backstepping control, image tracking, multiple uncertainties
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review