PMID- 33612275
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20211026
LR  - 20211026
IS  - 1879-2022 (Electronic)
IS  - 0019-0578 (Linking)
VI  - 118
DP  - 2021 Dec
TI  - Fast real-time SDRE controllers using neural networks.
PG  - 133-143
LID - S0019-0578(21)00102-6 [pii]
LID - 10.1016/j.isatra.2021.02.019 [doi]
AB  - This paper describes the implementation of fast state-dependent Riccati equation 
      (SDRE) control algorithms through the use of shallow and deep artificial neural 
      networks (ANN). Several ANNs are trained to replicate an SDRE controller 
      developed for a satellite attitude dynamics simulator (SADS) to display the 
      technique's efficacy. The neural controllers have reduced computational 
      complexity compared with the original SDRE controller, allowing its execution at 
      a significantly higher rate. One of the neural controllers was validated using 
      the SADS in a practical experiment. The experimental results indicate that the 
      training error is sufficiently small for the neural controller to perform 
      equivalently to the original SDRE controller.
CI  - Copyright (c) 2021 ISA. Published by Elsevier Ltd. All rights reserved.
FAU - da Costa, Romulo Fernandes
AU  - da Costa RF
AD  - Graduate Program in Electronic and Computer Engineering - Electronic Devices and 
      Systems, Electronic Engineering Division, Aeronautics Institute of Technology 
      (ITA), 50 Praca Marechal Eduardo Gomes, Sao Jose dos Campos, SP, 12228-900, 
      Brazil. Electronic address: Elromulo2006@yahoo.com.br.
FAU - Saotome, Osamu
AU  - Saotome O
AD  - Department of Applied Electronics, Electronic Engineering Division, Aeronautics 
      Institute of Technology (ITA), 50 Praca Marechal Eduardo Gomes, Sao Jose dos 
      Campos, SP, 12228-900, Brazil. Electronic address: osaotome@ele.ita.br.
FAU - Rafikova, Elvira
AU  - Rafikova E
AD  - Engineering, Modeling and Applied Social Sciences Center, Federal University of 
      ABC (UFABC), 5001 Av. dos Estados, Santo Andre - SP, 09210-580, Brazil. 
      Electronic address: elvirarafikova@ufabc.edu.br.
FAU - Machado, Renato
AU  - Machado R
AD  - Department of Telecommunications, Electronic Engineering Division, Aeronautics 
      Institute of Technology (ITA), 50 Praca Marechal Eduardo Gomes, Sao Jose dos 
      Campos, SP, 12228-900, Brazil. Electronic address: renatomachado@ieee.org.
LA  - eng
PT  - Journal Article
DEP - 20210216
PL  - United States
TA  - ISA Trans
JT  - ISA transactions
JID - 0374750
SB  - IM
OTO - NOTNLM
OT  - Deep learning
OT  - Neural control
OT  - SDRE control
OT  - Satellite attitude control
OT  - Stacked denoising autoencoders
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2021/02/23 06:00
MHDA- 2021/02/23 06:01
CRDT- 2021/02/22 05:48
PHST- 2019/12/13 00:00 [received]
PHST- 2021/01/22 00:00 [revised]
PHST- 2021/02/09 00:00 [accepted]
PHST- 2021/02/23 06:00 [pubmed]
PHST- 2021/02/23 06:01 [medline]
PHST- 2021/02/22 05:48 [entrez]
AID - S0019-0578(21)00102-6 [pii]
AID - 10.1016/j.isatra.2021.02.019 [doi]
PST - ppublish
SO  - ISA Trans. 2021 Dec;118:133-143. doi: 10.1016/j.isatra.2021.02.019. Epub 2021 Feb 
      16.