Auxiliary Input Design for Active Fault Detection via Deep Reinforcement Learning
Author:
Affiliation:
1. Tsinghua University,Department of Automation,Beijing,China,100084
Funder
National Natural Science Foundation of China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9693491/9693537/09693676.pdf?arnumber=9693676
Reference27 articles.
1. Stochastic Nonlinear Model Predictive Control with Active Model Discrimination: A Closed-Loop Fault Diagnosis Application
2. Input design for guaranteed fault diagnosis using zonotopes
3. Closed-loop input design for guaranteed fault diagnosis using set-valued observers
4. Active fault diagnosis: A multi-parametric approach
5. A hybrid stochastic-deterministic input design method for active fault diagnosis
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1. Fault Identification Enhancement with Reinforcement Learning (FIERL);2024 American Control Conference (ACC);2024-07-10
2. Active Fault Diagnosis Based on Dual-Observer for Leakage Detection and Estimation With Adaptive Auxiliary Signal Mechanism in Industrial Gas Networks;IEEE Transactions on Instrumentation and Measurement;2024
3. Reinforcement learning-based integrated active fault diagnosis and tracking control;ISA Transactions;2023-01
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