# A Review of Cognitive Control: Advancement, Definition, Framework, and Prospect
## 2 Cognitive Control Research Overview
### Cognitive Control in Manufacturing
In [48], an artificial cognitive control system was established based on the shared circuit model (SCM), which was verified to maintain cutting force stability during the drilling process by cognitively controlling the feed speed. SCM is a cognitive model that shows how subpersonal resources for control, mirroring, and simulation can enable the distinctive human sociocognitive skills of imitation, deliberation, and mind reading. In [49; 50], a learning algorithm based on \(Q\)-learning was designed for the architecture established in [48]. During the development of cognitive control, some scholars suggest that the cognitive control system should not wholly copy a cognitive model as its portability will be reduced and the cost of secondary design will increase. When designing a cognitive control system, more attention should be paid to the realization of cognitive functions rather than formalizations [10; 51]. Similar viewpoints can be found in [52; 53]. In [52], a cognitive safe controller that contained a strategic controller and an operational controller was designed, as shown in Figure 6. The mission of the strategic controller is to optimize the standard controller and generate control actions to keep the plant away from safety-critical states; the operational controller becomes active when a warning level (before reaching the critical state) is encountered, and the operational controller ensures that a critical state is never entered. The author has explained the working principles of the strategic controller and operational controller in detail. However, the method for realizing safety sensors and safety actuators cannot be found in the paper. In [53], a cognitive control system with memory, reasoning, and planning functions was realized based on the combination of the graph model, radio frequency identification technology, and the Dijkstra algorithm. The  
Figure 4: The cognitive control unit based on SOAR [46]. Figure 5: The cognitive agent based on BDI [47]. experimental results showed that the machine downtime during a 3 h test run fell by 41.4%, and the waiting time of the production cell decreased by 17.7%. The integration of cognitive control systems with manufacturing systems has attracted the attention of scholars. Since most modern manufacturing systems are CPSs, various open architectures have been formed for CPS to achieve unified access to systems software and hardware resources. When integrated with a CPS, the cognitive control system should follow the specifications of at least one of these open architectures. In [54], a cognitive control system was realized based on the production logistics and sustainability cockpit open architecture and the cognitive control architecture proposed in [48]. In [55], 5C architecture was employed to illustrate the relationship between cognitive control and CPS. The 5C structure is an open structure with five layers for the implementation of a CPS, as shown in Figure 7. The connection layer comprises sensors that collect data, the conversion layer extracts relevant information from the data, the cyber layer estimates the system state according to the relevant information, and the cognition layer presents the system's state to users and helps them make control decisions, which are finally executed at the configuration layer [56]. In [57], a cognitive control system that conformed to the ISA-95 standard [58] was built using the cognitive model of simulation of mental apparatus (SiMA), which cooperated with the manufacturing execution system to automatically improve the health status of the production system. In [59], a hierarchical cognitive control methodology was proposed. A hierarchical cognitive control system contains several cognitive controllers that solve problems at different levels. These controllers cascade together to accomplish production tasks. In [60], the cognitive control system was divided into four layers: two cognitive entity layers, a cognitive data-driven layer, and a cognitive communication and security layer.