Bat algorithm based control to decrease the control energy consumption and modified bat algorithm based control to increase the trajectory tracking accuracy in robots

From the control theory, the best control gain produces a balance between the trajectory tracking accuracy and control energy consumption. The random search of the bat algorithm is one alternative to find the best control gain. In this paper, (1) a bat algorithm based control is proposed to decrease...

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Veröffentlicht in:Neural networks Jg. 161; S. 437 - 448
1. Verfasser: Rubio, José de Jesús
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Ltd 01.04.2023
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ISSN:0893-6080, 1879-2782, 1879-2782
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Zusammenfassung:From the control theory, the best control gain produces a balance between the trajectory tracking accuracy and control energy consumption. The random search of the bat algorithm is one alternative to find the best control gain. In this paper, (1) a bat algorithm based control is proposed to decrease the control energy consumption in robots, where a bat algorithm is used to find the best control gain; and (2) a modified bat algorithm based control is proposed to increase the trajectory tracking accuracy in robots, where a modified bat algorithm is used to find the best control gain. The comparison between the two proposed controls and the simplex based control is illustrated for the trajectory tracking accuracy and control energy consumption in two robots. •A bat algorithm based control is proposed to decrease the control energy consumption in robots, where a bat algorithm is used to find the best control gain.•A modified bat algorithm based control is proposed to increase the trajectory tracking accuracy in robots, where a modified bat algorithm is used to find the best control gain.•The comparison between the two proposed controls and the simplex based control is illustrated for the trajectory tracking accuracy and control energy consumption in two robots.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2023.02.010