Fault-tolerant control for nonlinear offshore steel jacket platforms based on reinforcement learning

2022 ◽  
Vol 246 ◽  
pp. 110247
Author(s):  
Amin Ziaei ◽  
Hamed Kharrati ◽  
Afshin Rahimi
Keyword(s):  
Reinforcement Learning ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Steel Jacket

scholarly journals Fault-Tolerant Control of Degrading Systems with On-Policy Reinforcement Learning

2020 ◽  
Vol 53 (2) ◽  
pp. 13733-13738
Author(s):  
Ibrahim Ahmed ◽  
Marcos Quiñones-Grueiro ◽  
Gautam Biswas
Keyword(s):  
Reinforcement Learning ◽  
Fault Tolerant ◽  
Fault Tolerant Control

scholarly journals Reinforcement learning–based fault-tolerant control with application to flux cored wire system

2018 ◽  
Vol 51 (7-8) ◽  
pp. 349-359 ◽  
Author(s):  
Dapeng Zhang ◽  
Zhiwei Gao
Keyword(s):  
Reinforcement Learning ◽  
Control Method ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Sensor Faults ◽  
Test Bed ◽  
Operation Performance ◽  
Unexpected Events ◽  
Cored Wire ◽  
Wire System

Background: Processes and systems are always subjected to faults or malfunctions due to age or unexpected events, which would degrade the operation performance and even lead to operation failure. Therefore, it is motivated to develop fault-tolerant control strategy so that the system can operate with tolerated performance degradation. Methods: In this paper, a reinforcement learning -based fault-tolerant control method is proposed without need of the system model and the information of faults. Results and Conclusions: Under the real-time tolerant control, the dynamic system can achieve performance tolerance against unexpected actuator or sensor faults. The effectiveness of the algorithm is demonstrated and validated by the rolling system in a test bed of the flux cored wire.

Fault-Tolerant Control of Programmable Logic Controller-Based Production Systems With Deep Reinforcement Learning

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Jonas Zinn ◽  
Birgit Vogel-Heuser ◽  
Marius Gruber
Keyword(s):  
Reinforcement Learning ◽  
Production Systems ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Fault Recovery ◽  
Suggested Approach ◽  
System Availability ◽  
Automated Production ◽  
Multi Agent ◽  
End Effectors

Abstract Fault-tolerant control policies that automatically restart programable logic controller-based automated production system during fault recovery can increase system availability. This article provides a proof of concept that such policies can be synthesized with deep reinforcement learning. The authors specifically focus on systems with multiple end-effectors that are actuated in only one or two axes, commonly used for assembly and logistics tasks. Due to the large number of actuators in multi-end-effector systems and the limited possibilities to track workpieces in a single coordinate system, these systems are especially challenging to learn. This article demonstrates that a hierarchical multi-agent deep reinforcement learning approach together with a separate coordinate prediction module per agent can overcome these challenges. The evaluation of the suggested approach on the simulation of a small laboratory demonstrator shows that it is capable of restarting the system and completing open tasks as part of fault recovery.

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