###### Abstract
Due to the plethora of existing algorithms, the aim is not to present a complete overview of existing approaches and methods, but to highlight the most promising ones and the potential of their future development and applications to face modern industrial control and automation challenges, by means of emerging AI-based methodologies. In particular, the key role of the integration of AI methods in industrial control solutions is to strive for future self-optimizing production systems. For the successful implementation of self-optimizing machines, all components over all hierarchies must be optimally designed and operational. Figure 1 schematically illustrates the main element of self-optimizing production systems and forms the basis for the focus of the first sections in the present review and survey paper. Starting from a controller dedicated to a set of well-defined process variables, multiple aspects must be considered to enable an accurate and reliable control that is elementary for subsequent higher-level control structures. The first step of each controller is the direct or indirect measurement of the process variable under consideration. In order to obtain the most relevant information and controller inputs, sensor signals must be processed and elaborated accordingly. Since amount, type and quality of sensor signals can vary significantly, different methods are required to process them in an appropriate manner. In particular, trends towards the utilization of multiple sensors and the consequently increasing data availability require new developments towards suitable approaches for sensor and data fusion. Related current approaches and AI-based methods are discussed in Section 2. A further important element within optimized controllers and manufacturing applications are process models. Depending on the use case, these process models can be generated conventionally with multi-physics simulations as finite element methods or more recently, data-driven approaches enable the generation of process models based on the previously acquired data. However, not all process models are suitable for control applications. An overview of available approaches and recent developments concerning AI-based process models is given in Section 3. By processing the measurement values and querying process models, the optimizer determines the optimal controller output. Depending on the application, these processes must be performed very fast or even in real time and with as much autonomy as possible, requiring intelligent and specialized solutions. Therefore, promising AI-based methods to implement effective optimizers are presented in Section 4. A further concatenation of multiple optimal controllers enables an advanced control strategy over all hierarchical process levels. The main elements of self-optimizing machines are their capability to autonomously adapt to changing requirements and circumstances. Therefore, the task scheduler coupled with multiple cascaded control chains allows one to redefine and modify control objectives of control chains towards higher-level optimal objectives. Thereby, an example for the cascaded control chains could be the process control of an additive manufacturing process, where the first controller guarantees a constant energy density during the deposition process and the second controller acts layerwise by modulating the process parameter for the subsequent layer to obtain an equal deposition height. In an exemplary additive manufacturing process, the two control loops could represent the regulation of two laser sources, simultaneously building various features of a more complex part. A simplified overview of a self-optimizing framework is displayed in Figure 2.  
AI methods can enhance the self-optimization capacity of a cognitive industrial control system at different hierarchical and functional levels.