แนวคิดหลัก
Incorporating neural networks in event-triggered mechanisms optimizes communication in consensus problems while preserving stability.
บทคัดย่อ
The article discusses incorporating neural networks in event-triggered mechanisms (ETMs) for consensus problems. It proposes NN-ETM as a solution to optimize communication while ensuring stability. The structure, architecture, and training process of NN-ETM are detailed. Simulation results show the trade-off between error and communication rate based on the cost function parameter λ.
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Introduction
- ETMs reduce communication load in networked control applications.
- Design complexity increases for decentralized multi-agent setups.
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Data Extraction
- "This typically results in ad-hoc solutions, which may only work for the consensus protocols under consideration."
- "Advantageously combining these pieces of information is not trivial."
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Quotations
- "Unlike hand-crafted approaches, data-driven methods such as Neural Networks (NNs) have been used to work around standard feature engineering."
- "Moreover, we aim to incorporate them safely, providing guarantees of boundedness for the consensus error."
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Design Criteria for ETM in Consensus
- Input-to-State Stability criteria are derived.
- Interconnection of ISS consensus and ETM is analyzed.
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ISS Consensus
- Linear and nonlinear consensus protocols are discussed.
- Stability analysis under different ETMs is highlighted.
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Experiments
- Training process using Pytorch's autograd is explained.
- Fuzzy Event-Triggering Mechanism is introduced.
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Simulation Results
- Results show the effectiveness of trained NN-ETM on a network of 5 agents.
- Trade-off between error and communication rate is demonstrated.
สถิติ
"This typically results in ad-hoc solutions, which may only work for the consensus protocols under consideration."
"Advantageously combining these pieces of information is not trivial."
คำพูด
"Unlike hand-crafted approaches, data-driven methods such as Neural Networks (NNs) have been used to work around standard feature engineering."
"Moreover, we aim to incorporate them safely, providing guarantees of boundedness for the consensus error."