Learning event‐triggered control based on evolving data‐driven fuzzy granular models

This article proposes a data‐stream‐driven event‐triggered control strategy using evolving fuzzy models learned by granulation of input–output samples of nonlinear systems with unknown time‐varying dynamics. The evolving fuzzy model is obtained online from a data stream ensuring data coverage based...

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Bibliographic Details
Published in:International journal of robust and nonlinear control 2022-03, Vol.32 (5), p.2805-2827
Main Authors: Cordovil, Luiz A. Q., Coutinho, Pedro H. S., Bessa, Iury, Peixoto, Márcia L. C., Palhares, Reinaldo Martínez
Format: Article
Language:English
Subjects:
Algorithms
Control algorithms
Control systems design
Control theory
Controllers
Data transmission
data‐driven modeling
Event triggered control
Evolution
evolving fuzzy systems
Fuzzy control
fuzzy granular computing
Granulation
learning event‐triggered control
Machine learning
Model accuracy
Network control
Nonlinear systems
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Summary:This article proposes a data‐stream‐driven event‐triggered control strategy using evolving fuzzy models learned by granulation of input–output samples of nonlinear systems with unknown time‐varying dynamics. The evolving fuzzy model is obtained online from a data stream ensuring data coverage based on the principle of justifiable granularity and controlled by an event‐triggering learning mechanism dependent on the model accuracy. This evolving fuzzy model is used to design event‐triggered fuzzy controller to stabilize networked control systems while reducing the used communication resources. The event‐triggered learning mechanism is employed to determine the instants in which the event‐triggered fuzzy controller should be redesigned. Numerical examples illustrate the effectiveness of the proposed learning event‐triggered fuzzy control algorithm.
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.6024