Abstract
Industrial control systems play a central role in today’s manufacturing systems. Ongoing trends towards more flexibility and sustainability, while maintaining and improving production capacities and productivity, increase the complexity of production systems drastically. To cope with these challenges, advanced control algorithms and further developments are required. In recent years, developments in Artificial Intelligence (AI)-based methods have gained significantly attention and relevance in research and the industry for future industrial control systems. AI-based approaches are increasingly explored at various industrial control systems levels ranging from single automation devices to the real-time control of complex machines, production processes and overall factories supervision and optimization. Thereby, AI solutions are exploited with reference to different industrial control applications from sensor fusion methods to novel model predictive control techniques, from self-optimizing machines to collaborative robots, from factory adaptive automation systems to production supervisory control systems. The aim of the present perspective paper is to provide an overview of novel applications of AI methods to industrial control systems on different levels, so as to improve the production systems’ self-learning capacities, their overall performance, the related process and product quality, the optimal use of resources and the industrial systems safety, and resilience to varying boundary conditions and production requests. Finally, major open challenges and future perspectives are addressed.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference108 articles.
1. Innovative control of assembly systems and lines;Krüger;CIRP Ann.,2017
2. Jamwal, A., Agrawal, R., Sharma, M., and Giallanza, A. Industry 4.0 technologies for manufacturing sustainability: A systematic review and future research directions. Appl. Sci., 2021. 11.
3. Chui, M., Francisco, S., and Manyika, J. Notes from the Ai Frontier Insights from Hundreds of Use Cases. 2022.
4. A review of machine learning for the optimization of production processes;Weichert;Int. J. Adv. Manuf. Technol.,2019
5. Yang, L., and Zhu, M. Review on the status and development trend of AI industry. Proceedings of the 4th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA 2019).
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