States estimation and resilient control for non-Gaussian stochastic distribution control system under sensor attacks
Journal of the Franklin Institute, ISSN: 0016-0032, Vol: 361, Issue: 1, Page: 60-70
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Article Description
This paper studies states estimation and resilient control schemes based on model predictive control (MPC) algorithm for a class of Takagi–Sugeno (T–S) fuzzy stochastic distribution control (SDC) system subjected to sparse sensor attacks. Firstly, a T–S fuzzy model is used to approximate the dynamics of a non-Gaussian SDC system, where the outputs of the system is the output probability density functions (PDFs). Secondly, in order to estimate the states and attacks in the system, a fuzzy Luenberger observer is designed. In addition, based on the estimated states and attacks, the designed MPC resilient control method achieves a satisfied tracking performance. Finally, the feasibility of the estimation algorithm and resilient controller are verified by simulation.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0016003223007585; http://dx.doi.org/10.1016/j.jfranklin.2023.11.043; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85179582302&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0016003223007585; https://dx.doi.org/10.1016/j.jfranklin.2023.11.043
Elsevier BV
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