This paper presents a novel adaptive fault-tolerant control (AFTC) framework for systems with piezoelectric sensor patches, specifically targeting sensor faults and external disturbances. The proposed method ensures robust control of cantilever thick plates by integrating adaptive estimation to simultaneously handle sensor faults and system uncertainties, maintaining stability despite issues like drift, bias, loss of accuracy, and effectiveness. Unlike traditional approaches that address sensor faults individually, our method provides a comprehensive solution backed by Lyapunov-based stability analysis, demonstrating uniform ultimate boundedness under various fault conditions. Extensive simulations validate the controller's superior fault compensation and disturbance rejection compared to Backstepping Sliding Mode Control (BSMC). Under fault-free conditions, the baseline controller achieves accurate tracking, while the AFTC shows significant improvement in trajectory tracking and adaptation when faults are introduced, with minimal performance degradation. This work extends fault-tolerant control strategies to complex systems, including those involving piezoelectric elements, providing a foundation for future research in this area, which has been largely unexplored.
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http://dx.doi.org/10.1038/s41598-024-84420-1 | DOI Listing |
Adv Sci (Weinh)
January 2025
Key Laboratory of Optoelectronic Technology & Systems of Ministry of Education, International R&D Center of Micro-Nano Systems and New Materials Technology, Chongqing University, Chongqing, 400044, China.
Sound signals not only serve as the primary communication medium but also find application in fields such as medical diagnosis and fault detection. With public healthcare resources increasingly under pressure, and challenges faced by disabled individuals on a daily basis, solutions that facilitate low-cost private healthcare hold considerable promise. Acoustic methods have been widely studied because of their lower technical complexity compared to other medical solutions, as well as the high safety threshold of the human body to acoustic energy.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Department of Chemical Engineering, Faculty of Chemistry and Chemical Engineering, Babes-Bolyai University of Cluj-Napoca, 11 Arany János Street, 400028, Cluj-Napoca, Romania.
One of the leading challenges in Water Resource Recovery Facility monitoring and control is the poor data quality and sensor consistency due to the tough and complex circumstances of the process operation. This paper presents a new principal component analysis fault detection approach for the nitrate and nitrite concentration sensor based on Water Resource Recovery Facility measurements, together with the Fisher Discriminant Analysis identification of fault types. Five malfunction cases were considered: constant additive error, ramp changing error in time, incorrect amplification error, random additive error, and unchanging sensor value error.
View Article and Find Full Text PDFSci Rep
January 2025
School of Mechanical Engineering, Shiraz University, Shiraz, Fars, 7193616548, Iran.
This paper presents a novel adaptive fault-tolerant control (AFTC) framework for systems with piezoelectric sensor patches, specifically targeting sensor faults and external disturbances. The proposed method ensures robust control of cantilever thick plates by integrating adaptive estimation to simultaneously handle sensor faults and system uncertainties, maintaining stability despite issues like drift, bias, loss of accuracy, and effectiveness. Unlike traditional approaches that address sensor faults individually, our method provides a comprehensive solution backed by Lyapunov-based stability analysis, demonstrating uniform ultimate boundedness under various fault conditions.
View Article and Find Full Text PDFData Brief
December 2024
Department of Mechanical Engineering, Aalto University, Espoo, Finland.
Accurate system health state prediction through deep learning requires extensive and varied data. However, real-world data scarcity poses a challenge for developing robust fault diagnosis models. This study introduces two extensive datasets, Aalto Shim Dataset and Aalto Gear Fault Dataset, collected under controlled laboratory conditions, aimed at advancing deep learning-based fault diagnosis.
View Article and Find Full Text PDFMil Med
December 2024
Clinical and Operational Space Medicine Innovation Consortium (COSMIC), 59th Medical Wing Science and Technology, Lackland Air Force Base, TX 78236, USA.
Introduction: Military and commercial stakeholders are investing to explore the use of hypersonic aircraft and orbital spacecraft to transport cargo, medical supplies, passengers, and casualties. These vehicle platforms require periods of sustained acceleration, but to date, these dynamic forces have not been comprehensively considered in the environment of critical care patient movement because injured patients and advanced aeromedical evacuation (AE) equipment are rarely subjected to these conditions. While military AE equipment does undergo crash hazard acceleration testing, equipment functionality during or after sustained acceleration remains to be evaluated.
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