# A Review of Cognitive Control: Advancement, Definition, Framework, and Prospect
## 7 Main Issues, Research Directions, and Development Prospects of Cognitive Control  
### Main Issues of Cognitive Control  
As mentioned in Section 2, cognitive control has mainly been investigated in manufacturing, robotics, and CDS. The former two are application oriented, while cognitive control based on CDS initially focuses on theories and methods, and the outcomes are then applied to engineering systems. As an emerging intelligent control method, cognitive control faces many problems and difficulties during its development; these problems can be summarized in the following four aspects:  
Firstly, it is challenging to develop a general theory and method of cognitive control in the short term. Currently, cognitive control system design is primarily based on cognitive models. However, cognitive scientists have yet to obtain consistency in cognitive models. Although common elements exist in various cognitive models, their details are different. In addition, problems faced in different fields are not the same. Consequently, cognitive control methods differ not only in different fields but also within a single field.  
Because the algorithms in cognitive models are unsuitable for direct application in cognitive control systems, they need to be redesigned when establishing a cognitive control system based on cognitive models. However, there is no unified standard for the designation of algorithms, and sometimes, no suitable algorithm can be found when realizing a specific cognitive function, which leads to the failure of fully embodying the autonomous learning and optimization ability of cognitive control.  
Secondly, there are only a few applications related to cognitive control. Cognitive control is primarily applied in manufacturing and robotics; the research on cognitive control based on CDS is initially focused on theories and methods, and even though it has been applied to CBTC systems, microgrid systems, and vehicular radar and V2V communication systems, very few studies have been conducted. In addition, the portability of established cognitive control systems is low and it is challenging to apply them to other application cases without marked changes. It can be said that the application of cognitive control is more difficult than theoretical research, and instead, an abundance of experimental simulations have been carried out to verify the proposed research.  
Thirdly, algorithms in cognitive control systems, such as reinforcement learning, demand a lot of computing power, and the algorithm's training time is long in complex scenes. To meet the needs of the cognitive control system, more than the existing computer software and hardware is needed. Therefore, ensuring the real-time performance of cognitive control systems in complex scenes is a significant issue in practical applications.  
Finally, there is no uniform standard for the performance evaluation of cognitive-control systems. Owing to the differences in research backgrounds and goals of cognitive control, it is difficult to build a reliable evaluation system for cognitive control systems or make transverse comparisons for them.  
However, research on cognitive control has made great progress over the past ten years. We believe that, as research develops, cognitive control plays a more important role in the field of intelligent control.