Real-Time Parameter Identification for Forging Machine Using Reinforcement Learning

Zhang, Dapeng, Du, Lifeng and Gao, Zhiwei (2021) Real-Time Parameter Identification for Forging Machine Using Reinforcement Learning. Processes, 9 (10). p. 1848. ISSN 2227-9717

processes-09-01848-v2.pdf - Published Version
Available under License Creative Commons Attribution 4.0.

Download (6MB) | Preview
Official URL:


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.

Item Type: Article
Additional Information: Funding information: This research was funded by the Program of Science and Technology Commissioner, and National Nature Science Foundation of China, grant number 61673074.
Uncontrolled Keywords: parameter acquisition, mechanism model, reinforcement learning, forging machine
Subjects: H300 Mechanical Engineering
H700 Production and Manufacturing Engineering
H900 Others in Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Rachel Branson
Date Deposited: 21 Oct 2021 08:25
Last Modified: 21 Oct 2021 08:30

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics