# A Review of Cognitive Control: Advancement, Definition, Framework, and Prospect
## 5 Fundamental Framework of a Cognitive Control System
For example, cognitive actions could increase the transmission power and adjust the upper limit of the MAC layer's retransmission times, reducing the influence of uncertain factors on the wireless communication process. Cognitive actions are stored in the controlling memory to guide the control process. The cognitive controller is a critical part of the cognitive control system; however, as mentioned in Section 3, there is no unified method for cognitive controller designation. In manufacturing, the cognitive controller is realized through a combination of several modules, such as reasoning, planning, and decision-making, as shown in Figures 3 and 4. In cognitive control systems based on CDS, reinforcement learning algorithms are generally chosen as the algorithm of the cognitive controller because it is shown in cognitive science that reinforcement learning algorithms conform to the working mechanism of the prefrontal cortex and embody human cognitive characteristics [149; 150; 151]. Memory. Memory includes perceptual memory, controlling memory, and working memory. Both perceptual memory and controlling memory belong to long-term memories, storing information that is necessary for cognitive control, such as groups of physical parameters, perceptual results, and performed cognitive actions for a long time, which are regarded as experiences in cognitive science [152; 153] and play an important role when cognitive control systems face unexpected situations. Working memory is short-term memory and its content could come from the preceptor and cognitive controller, as well as perceptual memory and controlling memory. Working memory is fundamentally used to help select cognitive actions, but in practical applications, it could be used or not [154]. How working memory receives and exchanges information with other memories and modules and helps the cognitive controller select cognitive actions depending on the specific application scenarios is conceptually described in Figure 17.  
* Attention and intelligence. Attention and intelligence are not shown in Figure 17. Attention has its own physiological structure in the human brain [155; 156; 157; 158], which manifests the goal-oriented characteristics of the human cognitive process. However, control systems are designed for specific tasks, unlike humans, who need to control their own attention. The algorithm in cognitive control systems exhibits task-oriented characteristics; attention manifests itself in an algorithmic manner as perceptual attention in the perceptor and cognitive attention in the cognitive controller. Similar to attention, intelligence does not occupy a physical space within the cognitive control system; instead, its influence is distributed throughout the system, and thereby, it derives its information processing power by exploiting all feedback loops within the system. In short, we may say that the entire PAC is intelligent, and the cognitive control system is an intelligent control system that conforms to the human cognitive paradigm. In each PAC, the packet loss rate and time delay of the network are calculated by the perceptor, and then the network state value is calculated based on the packet loss rate and time delay. The value of the ideal network state is defined as 0, which means there is no packet loss and time delay in the network. The information gap is network state minus the ideal network state. Figure 18 illustrates the application of cognitive control which is used to adjust the control horizon of the model predictive-control algorithm and the number of MAC layer retransmissions, mitigating the influences of packet loss and time delay on the wireless network control system (WNCS) [159]. The \(Q\)-learning algorithm is used as the cognitive control algorithm; based on the information gap feedbacked by the perceptor, the cognitive controller selects the control horizon and the number of MAC layer retransmissions for the next PAC from the predefined parameters set for the purpose of reducing the information gap.