###### 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. control systems; industrial automation; artificial intelligence; machine learning; self-learning machine tools; adaptive production systems +
Footnote †: journal: Applied Soft  
plants [1]. Closed loop and real time control solutions are already widely used and are well established on various levels of production systems. However, with increasing complexity of the manufacturing systems and control requirements, further developments of control algorithms are indispensable to address the demand for future manufacturing systems. Promising approaches for improved industrial control systems are based on recent developments in artificial intelligence (AI). AI has obtained wide popularity in the past years and is one of the most pursued topics in research and industry. Progress in computing hardware and software has facilitated the collection of data and its exploration with AI for various use cases. Initiatives such as Industry 4.0 have further highlighted the potential of AI-based methods and have pushed them to the centre of attention in many fields [2]. A report from McKinsey & Company states that AI could contribute with a value of USD 3.6-6.6 trillion annually in the supply-chain management and manufacturing sector [3]. Especially in the area of manufacturing, AI-based developments further expand the capabilities and pave the way for more advanced industrial control systems. Current applications and trends are shown in the following section for various levels. In addition to existing works about applications of AI or machine learning in manufacturing and Industry 4.0 [4; 5; 6; 7], this work provides a perspective overview of the current state of the art, recent developments and future challenges for each manufacturing process and control level. Therefore, this paper is structured following a hierarchical and functional bottom-up approach: starting from the processing and elaboration of sensor signals to the job scheduling operations of the whole production plant. Thereby, the focus lies on identifying and illustrating most promising artificial intelligence-based methods coping with current challenges at each industrial control and automation level, including the related data acquisition, process modelling, real-time control and task scheduling operations.