# A Review of Cognitive Control: Advancement, Definition, Framework, and Prospect
## 1 Introduction  
Although encouraging accomplishments have been achieved in automatic control, scholars still strive to develop control methods toward human-level intelligence. With the widespread application of cyber-physical systems (CPSs), it is challenging to satisfy the robust and adaptive requirements of control systems owing to the unmodeled dynamics and uncertain environments. The adaptive capability of control methods based on analytical models is limited by some assumptions; the general intelligent control methods, such as neural network control and fuzzy control, are, in essence, function approximation methods, and the model structure of a system needs to be specified first before applying them [1, 2, 3, 4]. Sometimes, a reliable model or the complete state space of a system cannot be established or defined. To make systems work effectively in such situations, it is imperative to investigate alternative control methods. Humans can learn to deal with uncertainties and complete complex tasks, an ability which is called cognition in cognitive science [5, 6]. As the germination of [PERSON]'s Cybernetics, it was suggested in [7] that humans and machines shared no fundamental differences, and the methods for studying them were similar, which laid the foundation for combining theories of control and cognitive science. Simply put, cognitive control is theapplication of cognitive science theories to the design of control systems, enabling them with cognitive abilities to learn and optimize themselves like humans. Cognitive control has been studied from various perspectives. In [8], the cognitive control system was considered a control system that knew what it was undertaking and could actively adjust its behaviors rather than passively respond to input. In [9; 10], the concept of cognitive technical system (CTS) was explained, that is, CPS with cognitive control capability. In [11], the idea of cognitive automation (CA) was introduced, and a cognitive system architecture (COSA) for CA was established based on the state, operator, and result (SOAR) cognitive model. In [12], cognitive control based on the cognitive dynamic system (CDS) was proposed, which corresponds to [PERSON]'s cognition paradigm [13], embodying four aspects of human cognition: the perception-action cycle (PAC), memory, attention, and intelligence. Actually, since the introduction and implementation of Industry 4.0, revolutionary changes have taken place in social life and production. Based on CPS and characterized as being networking-data driven, Industry 4.0 promotes the deep integration of the digital world and the physical world, making industrial systems more intelligent [14; 15; 16]. In recent years, Industry 5.0 has received people's attention as an emerging concept, which is a further development of Industry 4.0, highlighting the central position of humans in industrial processes as it is the integration of humans, information systems, and physical systems [17; 18; 19; 20]. Industry 5.0 research involves many aspects, one of which is the deep cooperation between humans and machines. Industrial systems are autonomous to a certain extent, and humans can provide professional knowledge that systems cannot obtain and can respond to problems that systems do not know or are not aware of. Humans and machines cooperate to achieve key performance indicators [21; 22; 23]. The changes in the organization and operation of traditional industrial systems caused by Industry 4.0 and Industry 5.0 have made demands of some key capabilities for systems, such as self-perception, self-learning, self-diagnosis and self-optimization. Cognitive control organizes control systems according to the human cognition paradigm. Compared with other intelligent control methods, the intelligence of a cognitive control system is not limited to its controller; instead, the intelligence is embodied throughout the whole system.