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
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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.
| Item Type: | Article |
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| 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 |
| URI: | http://nrl.northumbria.ac.uk/id/eprint/47529 |
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