Real-Time Parameter Identification for Forging Machine Using Reinforcement Learning

It is a challenge to identify the parameters of a mechanism model under real-time operating conditions disrupted by uncertain disturbances due to the deviation between the design requirement and the operational environment. In this paper, a novel approach based on reinforcement learning is proposed...

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Bibliographic Details
Published in:Processes 2021-10, Vol.9 (10), p.1848
Main Authors: Zhang, Dapeng, Du, Lifeng, Gao, Zhiwei
Format: Article
Language:English
Subjects:
Algorithms
Batch processes
Control algorithms
Disturbances
Forging
Hydraulics
Learning
Learning algorithms
Mathematical models
Methods
Noise
Parameter estimation
Parameter identification
Real time
Reinforcement
Values
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Summary:It is a challenge to identify the parameters of a mechanism model under real-time operating conditions disrupted by uncertain disturbances due to the deviation between the design requirement and the operational environment. In this paper, a novel approach based on reinforcement learning is proposed for forging machines to achieve the optimal model parameters by applying the raw data directly instead of observation window. This approach is an online parameter identification algorithm in one period without the need of the labelled samples as training database. It has an excellent ability against unknown distributed disturbances in a dynamic process, especially capable of adapting to a new process without historical data. The effectiveness of the algorithm is demonstrated and validated by a simulation of acquiring the parameter values of a forging machine.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr9101848