Multi-agent sensor fault diagnosis method and system based on adaptive learning
By employing adaptive learning techniques and radial basis function neural networks, the problem of system unreliability caused by sensor failures in multi-agent systems is solved, thereby improving the accuracy of sensor fault diagnosis and system stability, and enhancing the system's safety and reliability.
CN116519037BActive Publication Date: 2026-05-01SHANDONG UNIV
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Patent Information
- Application Number
- CN202310259996.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-03-14
- Publication Date
- 2026-05-01
- Estimated Expiration
- 2043-03-14
AI Technical Summary
Technical Problem
Sensor failures in multi-agent systems can lead to system unreliability, system crashes, and even catastrophic accidents.
Method used
Adaptive learning technology is employed to acquire sensor fault information through an adaptive observer, a radial basis function neural network is used to approximate the sensor fault, and a Lyapunov function is constructed to verify the boundedness of the error, thereby ensuring the accuracy of sensor fault diagnosis and system stability.
Benefits of technology
This achieves final, consistent, and boundedness in sensor fault diagnosis, improving system safety and reliability and reducing the impact of sensor faults on the system.
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Abstract
The application relates to the technical field of multi-agent systems, and provides a multi-agent sensor fault diagnosis method and system based on adaptive learning, which comprises the following steps: acquiring control signals and outputs of each multi-agent subsystem; based on the control signals and the outputs, an adaptive observer is used to obtain a state vector of the adaptive observer; by comparing the state vector of the adaptive observer with a state vector of the multi-agent subsystem, fault information of a sensor corresponding to the multi-agent subsystem is obtained; wherein the adaptive observer ensures that a tracking error and a weight vector error are bounded, the tracking error is a difference value between an output estimation of the multi-agent subsystem and an output of the multi-agent subsystem, and the weight vector error is a difference value between an estimated weight of a neural network in the adaptive observer and an ideal value; and the final consistent boundedness of identification errors is ensured.
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