Due to the limitations of human intelligence and artificial intelligence, developing hybrid augmented intelligence based on human-machine collaboration is one of the main research directions for the new generation of artificial intelligence, and the design of collaborative control algorithms is the core issue in achieving such intelligence. Therefore, a review of the current research status of human-machine collaborative enhanced intelligent control systems was provided in this article. Based on the black box characteristics of human behavior, the human behavior modeling methods of human-in-the-loop control systems were systematically sorted out and the advantages, disadvantages, and applicability of various modeling methods were analyzed. For the implementation of human-machine collaborative augmented intelligent control, the control design methods of machines collaborating with humans under different control theory frameworks were elaborated in detail. The scalability of human-machine collaborative control technology in the field of multi-agent systems and the evaluation methods of hybrid intelligence in the human-machine collaborative control systems were investigated and discussed. In addition, the application scenarios of human-machine collaborative augmented intelligent control methods in medical, industrial, military and other fields were presented. The prospects for human-machine collaborative augmented intelligent control research with the support of new technologies such as large models and embodied learning were presented.