The real-time fault detection and diagnosis algorithm of a liquid rocket engine is the basis of online reconfiguration of guidance and the control system of a launch vehicle, which is directly related to the success or failure of space mission. Based on previous related works, this paper carries out comparative experimental studies of relevant intelligent algorithm models for real-time fault detection engineering application requirements of a liquid hydrogen–oxygen rocket engine. Firstly, the working state and detection parameters’ selection of a hydrogen–oxygen engine are analyzed, and the proposed three real-time intelligent fault detection algorithm model design methods are elaborated again. Fault detection calculation and analysis are carried out through normal test data and fault test data. The comparative analysis results of real-time intelligent fault detection algorithm models is presented from three dimensions: detection time, fault detection, and stability and consistency. Finally, based on a correlation analysis, a comprehensive intelligent fault diagnosis model design framework is given to further solve the requirements of real-time fault detection and diagnosis engineering development of a liquid rocket engine, a complex piece of equipment.

Cooperative formation control of unmanned ground vehicles (UGVs) has become one of the important research hotspots in the application of UGV and attracted more and more attention in the military and civil fields. Compared with traditional formation control algorithms, reinforcement-learning-based algorithms can provide a new solution with a lower complexity for real-time formation control by equipping UGVs with artificial intelligence. Therefore, in this paper, a distributed deep-reinforcement-learning-based cooperative formation control algorithm is proposed to solve the navigation, maintenance, and obstacle avoidance tasks of UGV formations. More importantly, the hierarchical triangular formation structure and the newly designed Markov decision process for UGV formations of leader and follower attributes make the control strategy learned by the algorithm reusable, so that the formation can arbitrarily increase the number of UGVs and realize a more flexible expansion. The effectiveness and scalability of the algorithm is verified by formation simulation experiments of different scales.