Adaptive Secure State Estimation for Cyber-Physical Systems With Low Memory Cost

2020 ◽  
Vol 7 (4) ◽  
pp. 1621-1632
Author(s):  
Liwei An ◽  
Guang-Hong Yang
Author(s):  
Xiaolin Wang ◽  
Jianping He ◽  
Shanying Zhu ◽  
Cailian Chen ◽  
Xinping Guan

2022 ◽  
Author(s):  
Chengwei Wu ◽  
Weiran Yao ◽  
Guanghui Sun ◽  
Ligang Wu

2019 ◽  
Vol 30 (11) ◽  
pp. 4303-4330 ◽  
Author(s):  
Nicola Forti ◽  
Giorgio Battistelli ◽  
Luigi Chisci ◽  
Bruno Sinopoli

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Yongzhen Guo ◽  
Baijing Han ◽  
Weiping Wang ◽  
Manman Yuan

This paper is concerned with the security state estimation and event-triggered control of cyber-physical systems (CPSs) under malicious attack. Aiming at this problem, a finite-time observer is designed to estimate the state of the system successfully. Then, according to the state information, the event-triggered controller is designed through the event-triggered communication. It is proved that the system is uniformly and finally bounded. Finally, the effectiveness of the proposed method is verified by a simulation example.


2014 ◽  
Vol 62 (15) ◽  
pp. 3911-3923 ◽  
Author(s):  
Siddharth Deshmukh ◽  
Balasubramaniam Natarajan ◽  
Anil Pahwa

2018 ◽  
Vol 41 (6) ◽  
pp. 1571-1579 ◽  
Author(s):  
Hao Zhang ◽  
Chen Peng ◽  
Hongtao Sun ◽  
Dajun Du

This paper investigates the state estimation problem for cyber physical systems under sparse attacks. Firstly, the fundamental state estimation problem is transferred to an optimization problem with a unique solution. Secondly, an adaptive estimation method for sparse attacks is proposed, which convergence property is well proved. The advantage of proposed method is that the step-size can be adaptively adjusted based on the dynamic estimation errors. Therefore, the computing time is less than some existing methods while guaranteeing the desired performance. Then, a suitable state feedback is designed to improve the computing speed while enhancing the resiliency for the destroyed system. Finally, the speed performance and accuracy of proposed algorithm are verified by two numerical examples.


2017 ◽  
Vol 16 (11) ◽  
pp. 7560-7573
Author(s):  
Shuping Gong ◽  
Liang Li ◽  
Ju Bin Song ◽  
Husheng Li

Author(s):  
Gabriella Fiore ◽  
Young Hwan Chang ◽  
Qie Hu ◽  
Maria Domenica Di Benedetto ◽  
Claire J. Tomlin

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