Data-Driven Fault-Tolerant Tracking Control for Linear Parameter-Varying Systems
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Data-Driven_Fault-Tolerant_Tracking_Control_for_Linear_Parameter-Varying_Systems.pdf
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ArticleKAUST Department
Computer, Electrical and Mathematical Science and Engineering (CEMSE) DivisionComputer Science Program
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2022-06-21Permanent link to this record
http://hdl.handle.net/10754/679242
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This article proposes a data-driven passive fault-tolerant tracking controller for single-input single-output (SISO) discrete-time linear systems with slowly-varying unknown parameters in the presence of actuator faults. Initially, a parameterized controller is considered. Using the internal model principle, the controller structure is determined such that the passive-fault-tolerance and tracking objectives are achieved. The obtained fixed-structure controller is utilized for both normal and faulty conditions without using any feedback from the fault information. Thus, the controller is simple to implement and responds fast to the effects of faults. Then, using a data-driven technique and based on the input/output (I/O) data, the controller parameters are adjusted online to tackle the problem of parameter variation. A data-based constraint on the controller parameters is proposed for ensuring the stability of the closed-loop system. The proposed technique is extended to multi-input multi-output (MIMO) systems using the sequential loop closing concept and the relay auto-tuning method. Simulation results of applying the proposed controller to a direct current (DC) servo motor and a three-tank system demonstrate its effectiveness.Citation
Karimi, Z., Batmani, Y., Khosrowjerdi, M. J., & Konstantinou, C. (2022). Data-Driven Fault-Tolerant Tracking Control for Linear Parameter-Varying Systems. IEEE Access, 1–1. https://doi.org/10.1109/access.2022.3184690Publisher
IEEEJournal
IEEE AccessAdditional Links
https://ieeexplore.ieee.org/document/9801860/https://ieeexplore.ieee.org/document/9801860/
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9801860
ae974a485f413a2113503eed53cd6c53
10.1109/ACCESS.2022.3184690
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Except where otherwise noted, this item's license is described as Archived with thanks to IEEE Access This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/