PMID- 33852399
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20221006
IS  - 2162-2388 (Electronic)
IS  - 2162-237X (Linking)
VI  - 33
IP  - 10
DP  - 2022 Oct
TI  - Adaptive NN Finite-Time Resilient Control for Nonlinear Time-Delay Systems With 
      Unknown False Data Injection and Actuator Faults.
PG  - 5416-5428
LID - 10.1109/TNNLS.2021.3070623 [doi]
AB  - This article considers neural network (NN)-based adaptive finite-time resilient 
      control problem for a class of nonlinear time-delay systems with unknown fault 
      data injection attacks and actuator faults. In the procedure of recursive design, 
      a coordinate transformation and a modified fractional-order command-filtered 
      (FOCF) backstepping technique are incorporated to handle the unknown false data 
      injection attacks and overcome the issue of "explosion of complexity" caused by 
      repeatedly taking derivatives for virtual control laws. The theoretical analysis 
      proves that the developed resilient controller can guarantee the finite-time 
      stability of the closed-loop system (CLS) and the stabilization errors converge 
      to an adjustable neighborhood of zero. The foremost contributions of this work 
      include: 1) by means of a modified FOCF technique, the adaptive resilient control 
      problem of more general nonlinear time-delay systems with unknown cyberattacks 
      and actuator faults is first considered; 2) different from most of the existing 
      results, the commonly used assumptions on the sign of attack weight and prior 
      knowledge of actuator faults are fully removed in this article. Finally, two 
      simulation examples are given to demonstrate the effectiveness of the developed 
      control scheme.
FAU - Song, Shuai
AU  - Song S
FAU - Park, Ju H
AU  - Park JH
FAU - Zhang, Baoyong
AU  - Zhang B
FAU - Song, Xiaona
AU  - Song X
LA  - eng
PT  - Journal Article
DEP - 20221005
PL  - United States
TA  - IEEE Trans Neural Netw Learn Syst
JT  - IEEE transactions on neural networks and learning systems
JID - 101616214
SB  - IM
EDAT- 2021/04/15 06:00
MHDA- 2021/04/15 06:01
CRDT- 2021/04/14 17:12
PHST- 2021/04/15 06:00 [pubmed]
PHST- 2021/04/15 06:01 [medline]
PHST- 2021/04/14 17:12 [entrez]
AID - 10.1109/TNNLS.2021.3070623 [doi]
PST - ppublish
SO  - IEEE Trans Neural Netw Learn Syst. 2022 Oct;33(10):5416-5428. doi: 
      10.1109/TNNLS.2021.3070623. Epub 2022 Oct 5.