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Learning event-triggered control based on evolving data-driven fuzzy granular models
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DOI：10.1002/rnc.6024.png)
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Abstract
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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.
Keywords:
data-driven modeling
evolving fuzzy systems
fuzzy granular computing
learning event-triggered control
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