To improve the learning process of multi-agent system and the robustness of the system to external disturbances， an iterative learning consensus control algorithm with feedback control was proposed. Firstly， the learning process of agents was improved by sharing the control input information among agents， and the robustness of the system was improved by designing a feedback controller when there were non-iterative repetitive disturbances outside the system. Then， by using the contraction mapping method， the system consensus was analyzed， and the convergence condition of the algorithm was derived strictly. Finally， the correctness and effectiveness of the algorithm was verified through simulations. Compared with the P-type algorithm， the improved algorithm has higher convergence speed and smoother convergence curve in the presence of external disturbances.

With the help of blockchain and other emerging technologies， cryptocurrencies are decentralized， autonomous and cross-border. Research on cryptocurrency regulatory technologies is not only helpful to fight criminal activities based on cryptocurrencies， but also helpful to provide feasible supervision schemes for the expansion of blockchain technologies in other fields. Firstly， based on the application characteristics of cryptocurrency， the Generation， Exchange and Circulation （GEC） cycle theory of cryptocurrency was defined and elaborated. Then， the frequent international and domestic crimes based on cryptocurrencies were analyzed in detail， and the research status of cryptocurrency security supervision technologies in all three stages was investigated and surveyed as key point. Finally， the cryptocurrency regulatory platform ecology systems and current challenges faced by the regulatory technologies were summarized， and the future research directions of cryptocurrency regulatory technologies were prospected in order to provide reference for subsequent research.