A Sensor-Aided System for Physical Perfect Control Applications in the Continuous-Time Domain.
continuous-time systems
perfect control
practical implementation
real-life plant
state-space description
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
09 Feb 2023
09 Feb 2023
Historique:
received:
15
01
2023
revised:
06
02
2023
accepted:
07
02
2023
entrez:
28
2
2023
pubmed:
1
3
2023
medline:
1
3
2023
Statut:
epublish
Résumé
The recently introduced continuous-time perfect control algorithm has revealed a great potential in terms of the maximum-speed and maximum-accuracy behaviors. However, the discussed inverse model-originated control strategy is associated with considerable energy consumption, which has exceeded a technological limitation in a number of industrial cases. In order to prevent such an important drawback, several solutions could be considered. Therefore, an innovative perfect control scheme devoted to the multivariable real-life objects is investigated in this paper. Henceforth, the new IMC-related approach, strongly supported by the vital sensor-aided system, can successfully be employed in every real-time engineering task, where the precision of conducted processes plays an important role. Theoretical and practical examples strictly confirm the big implementation potential of the new established method over existing ones. It has been seen that the new perfect control algorithm outperforms the classical control law in the form of LQR (considered in two separate ways), which is clearly manifested by almost all simulation examples. For instance, in the case of the multi-tank system, the performance indices ISE, RT, and MOE for LQR without an integration action have been equal to 2.431, 2.4×102, and 3.655×10-6, respectively, whilst the respective values 1.638, 1.58×102, and 1.514×10-7 have been received for the proposed approach.
Identifiants
pubmed: 36850545
pii: s23041947
doi: 10.3390/s23041947
pmc: PMC9963907
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Références
Sensors (Basel). 2022 Jan 27;22(3):
pubmed: 35161746
ISA Trans. 2016 Mar;61:318-328
pubmed: 26838675
IEEE Trans Neural Netw Learn Syst. 2020 Nov;31(11):4451-4460
pubmed: 31869807
Sensors (Basel). 2019 May 29;19(11):
pubmed: 31146463
ACS Sens. 2019 Feb 22;4(2):268-280
pubmed: 30623644
Sensors (Basel). 2021 Jul 12;21(14):
pubmed: 34300503