Advances in Artificial Intelligence Methods Applications in Industrial Control Systems: Towards Cognitive Self-Optimizing Manufacturing Systems

Industrial control systems play a central role in today’s manufacturing systems. Ongoing trends towards more flexibility and sustainability, while maintaining improving production capacities productivity, increase the complexity of drastically. To cope with these challenges, advanced algorithms further developments are required. In recent years, Artificial Intelligence (AI)-based methods have gained significantly attention relevance research industry for future industrial AI-based approaches increasingly explored at various levels ranging from single automation devices to real-time complex machines, processes overall factories supervision optimization. Thereby, AI solutions exploited reference different applications sensor fusion novel model predictive techniques, self-optimizing machines collaborative robots, factory adaptive supervisory The aim present perspective paper is provide an overview on levels, so as improve systems’ self-learning capacities, their performance, related process product quality, optimal use resources safety, resilience varying boundary conditions requests. Finally, major open challenges perspectives addressed.