REACT-AGV
Resilient Estimation and Adaptive Control Technology for Automated Guided Vehicles
The PHC REACT project, in collaboration with ITB (Indonesia), is developing resilient control methods for AGVs, combining artificial intelligence, anomaly detection and adaptive control to ensure their safety, reliability and robustness in the face of failures and cyber-attacks.
Introduction
Automated guided vehicles (AGVs) are a key component of industrial automation and supply chains in Industry 4.0 and 5.0. However, their autonomous or remote-controlled operation exposes them to risks that could compromise their safety, reliability and performance.
The main scientific challenges are:
- Hardware failures: detecting and isolating faults in sensors and actuators whilst maintaining system performance.
- Cyber-attacks: ensuring resilience against data tampering, the injection of false information and communication disruptions.
- Uncertainties and disturbances: ensuring robustness in the face of modelling errors, load variations, external disturbances and transmission delays.
- Reliable state estimation: developing advanced observers incorporating artificial intelligence to reconstruct system states from incomplete or corrupted data.
- Stability and adaptive control: designing control laws that guarantee stability and safety despite the simultaneous occurrence of faults and attacks.
- Authority sharing: ensuring a safe and seamless transition between autonomous control, remote operation and human intervention.
The project aims to develop a unified framework for fault-tolerant and resilient control, combining diagnostics, AI-assisted estimation and adaptive control. The proposed approaches will be experimentally validated on the ITB’s AGV platform, in order to demonstrate their effectiveness under realistic conditions and to contribute to the development of safer and more reliable industrial autonomous systems.
In brief
| Department(s) | Partner(s) | Total amount |
|---|---|---|
| Automation | €12k | |
| Main sponsor | Outreach | Date(s) |
| Funded by Campus France as part of the PHC Nusantara 2026 Call for Proposals | International | 2026 - 2028 |
Keywords
Autonomous Guided Vehicles (AGVs); fault-tolerant control; adaptive control; distributed control; resilient estimation; fault detection and isolation; unknown-input observer; artificial intelligence-assisted observer; safe learning; Lyapunov stability; linear matrix inequalities (LMIs); anomaly detection.