Model-free Adaptive Optimal Control of Episodic Fixed-horizon Manufacturing Processes Using Reinforcement Learning-Reference-Cited by-同舟云学术

Model-free Adaptive Optimal Control of Episodic Fixed-horizon Manufacturing Processes Using Reinforcement Learning

Published:2019-12-26 Issue:6 Volume:18 Page:1593-1604
ISSN:1598-6446
Container-title:International Journal of Control, Automation and Systems
language:en
Short-container-title:Int. J. Control Autom. Syst.

Author:

Dornheim Johannes^ORCID,Link Norbert,Gumbsch Peter

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Control and Systems Engineering

Link

http://link.springer.com/content/pdf/10.1007/s12555-019-0120-7.pdf

Reference36 articles.

1. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, MIT press, 2018.

2. S. Tommerup and B. Endelt, “Experimental verification of a deep drawing tool system for adaptive blank holder pressure distribution,” Journal of Materials Processing Technology, vol. 212, no. 11, pp. 2529–2540, 2012.

3. C. P. Singh and G. Agnihotri, “Study of deep drawing process parameters: A review,” International Journal of Scientific and Research Publications, vol. 5, no. 1, pp. 2250–3153, 2015.

4. A. Wifi and A. Mosallam, “Some aspects of blankholder force schemes in deep drawing process,” Journal of Achievements in Materials and Manufacturing Engineering, vol. 24, no. 1, pp. 315–323, 2007.

5. R. Bellman, “Dynamic programming,” Science, vol. 153, no. 3731, pp. 34–37, 1966.

Cited by 34 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Improved Data-driven Adaptive Control Structure Against Input and Output Saturation;International Journal of Control, Automation and Systems;2024-08-31

2. Game-based Optimized Backstepping Control for Strict-feedback Systems With Input Constraints;International Journal of Control, Automation and Systems;2024-08

3. Data-driven adaptive optimal control for discrete-time linear time-invariant systems;International Journal of Systems Science;2024-06-10

4. Application of a Hybrid Improved Particle Swarm Algorithm for Prediction of Cutting Energy Consumption in CNC Machine Tools;International Journal of Control, Automation and Systems;2024-05-29

5. Quality 4.0: Learning quality control, the evolution of SQC/SPC;Quality Engineering;2024-05-28