Zero‐sum game‐based H∞ optimal finite‐time prescribed performance control for nonlinear multiagent systems with actuator faults

This article proposes an H∞$$ {H}_{\infty } $$ optimal leader‐follower consensus control protocol with finite‐time prescribed performance for nonlinear multiagent systems suffering from the actuator fault based on the zero‐sum game framework. First, to guarantee the specified performance of the syst...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International journal of robust and nonlinear control Ročník 34; číslo 13; s. 9019 - 9039
Hlavní autoři: Zhang, Bowen, Zhang, Linchuang, Pan, Yingnan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bognor Regis Wiley Subscription Services, Inc 10.09.2024
Témata:
Actuators
Adaptive algorithms
adaptive dynamic programming
Control systems design
Dynamic programming
Errors
finite‐time prescribed performance control
H-infinity control
Multiagent systems
Neural networks
Nonlinear control
nonlinear multiagent systems
Nonlinear systems
Performance indices
Robot arms
Transformations (mathematics)
zero‐sum game
ISSN:1049-8923, 1099-1239
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This article proposes an H∞$$ {H}_{\infty } $$ optimal leader‐follower consensus control protocol with finite‐time prescribed performance for nonlinear multiagent systems suffering from the actuator fault based on the zero‐sum game framework. First, to guarantee the specified performance of the system within a finite time frame, a novel error transformation function (ETF) is constructed. The ETF proposed in this paper can adjust the mapping range of error transformation to achieve better error convergence performance, thereby enhancing the flexibility of the controller design. Then, to compensate for the negative impact of actuator fault, an error‐related item is separated from the optimal performance index function to design an online fault estimator, which can obtain a better error estimation accuracy. Furthermore, to lessen the need for difficult‐to‐verify persistent excitation signal in adaptive dynamic programming algorithm, both historical and real‐time data are applied to adjust the updating law of neural network weight. Moreover, it demonstrates that the signals within the closed‐loop system are bounded and the expected finite‐time prescribed performance can be ensured in the presence of faults. Finally, a numerical example of multiple single‐link robots manipulator system is executed to validate the efficacy of the developed scheme.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.7445