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Data-Driven Event-Triggered Control for Nonlinear Multi-Agent Systems With Uniform Quantization
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AI
DOI：10.1109/TCSII.2023.3305946.png)
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Abstract
En 中文
In this brief, a data-driven event-triggered control algorithm is developed for the unknown discrete-time nonlinear multi-agent systems (MASs) with encoding-decoding uniform quantization. Firstly, an event-triggered condition for MASs is established based on model-free adaptive control (MFAC) to reduce the update times of the controller. Secondly, an encoding-decoding uniform quantization mechanism is designed to compress the volume of communication data between agents. Then, an event-triggered MFAC algorithm is proposed for MASs with uniform quantization, which is completely data-driven without relying on any information from the system model or structure. Through convergence analysis, it is proved that the tracking errors of MASs are bounded. Finally, simulation results illustrate the feasibleness of the proposed data-driven algorithm.
Keywords:
Quantization (signal)
Adaptation models
Decoding
Multi-agent systems
Bandwidth
Process control
Mathematical models
Data-driven algorithm
event-triggered control
model-free adaptive control
multi-agent systems
uniform quantization
Journal
I
IF:
4.9
Papers:
8.8K
Citations:
2.5W