Real-Time Parameter Identification for Forging Machine Using Reinforcement Learning

Dapeng Zhang, Lifeng Du, Zhiwei Gao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Downloads (Pure)

Abstract

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.
Original languageEnglish
Article numbere1848
Number of pages19
JournalProcesses
Volume9
Issue number10
DOIs
Publication statusPublished - 18 Oct 2021

Fingerprint

Dive into the research topics of 'Real-Time Parameter Identification for Forging Machine Using Reinforcement Learning'. Together they form a unique fingerprint.

Cite this