Health monitoring and fault diagnosis of liquid rocket engine (LRE) are the most important concerning issue for the safety of rocket’s flying, especially for the man-carried aerospace engineering. Based on the sensor measurement signals of a certain type of hydrogen-oxygen rocket engine, this paper proposed a real-time fault detection approach using a genetic algorithm-based least squares support vector regression (GA-LSSVR) algorithm for the real-time fault detection of the rocket engine. In order to obtain effective training samples, the data is normalized in this paper. Then, the GA-LSSVR algorithm is derived through comprehensive considerations of the advantages of the Support Vector Regression (SVR) algorithm and Least Square Support Vector Regression (LSSVR). What is more, this paper provided the genetic algorithm to search for the optimal LSSVR parameters. In the end, the computational results of the suggested approach using the rocket practical experimental data are given out. Through the analysis of the results, the effectiveness and the detection accuracy of this presented real-time fault detection method using LSSVR GA-optimized is verified. The experiment results show that this method can effectively diagnose this hydrogen-oxygen rocket engine in real-time, and the method has engineering application value.

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.

Multi-unmanned systems have demonstrated significant applications across various fields under complex or extreme operating environments. In order to make such systems highly efficient and reliable, cooperative decision-making methods have been utilized as a critical technology for successful future applications. However, current multi-agent decision-making algorithms pose many challenges, including difficulties understanding human decision processes, poor time efficiency, and reduced interpretability. Thus, a real-time online collaborative decision-making model simulating human cognition is presented in this paper to solve those problems under unknown, complex, and dynamic environments. The provided model based on the Soar cognitive architecture aims to establish domain knowledge and simulate the process of human cooperation and adversarial cognition, fostering an understanding of the environment and tasks to generate real-time adversarial decisions for multi-unmanned systems. This paper devised intricate forest environments to evaluate the collaborative capabilities of agents and their proficiency in implementing various tactical strategies while assessing the effectiveness, reliability, and real-time action of the proposed model. The results reveal significant advantages for the agents in adversarial experiments, demonstrating strong capabilities in understanding the environment and collaborating effectively. Additionally, decision-making occurs in milliseconds, with time consumption decreasing as experience accumulates, mirroring the growth pattern of human decision-making.

With the advantages of long-range flight and high payload capacity, large fixed-wing UAVs are often used in anti-terrorism missions, disaster surveillance, and emergency supply delivery. In the existing research, there is little research on the 3D model design of the V-tail fixed-wing UAV and 3D flight environment modeling. The study focuses on designing a comprehensive simulation environment using Gazebo and ROS, referencing existing large fixed-wing UAVs, to design a V-tail aircraft, incorporating realistic aircraft dynamics, aerodynamics, and flight controls. Additionally, we present a simulation environment modeling approach tailored for obstacle avoidance in no-fly zones, and have created a 3D flight environment in Gazebo, generating a large-scale terrain map based on the original grayscale heightmap. This terrain map is used to simulate potential mountainous terrain threats that a fixed-wing UAV might encounter during mission execution. We have also introduced wind disturbances and other specific no-fly zones. We integrated the V-tail fixed-wing aircraft model into the 3D flight environment in Gazebo and designed PID controllers to stabilize the aircraft’s flight attitude.