Abstract

Micro-turbojet is widely used in civil aviation due to high thrust-to-weight ratio, superior combustion efficiency and low fuel consumption. In order to exert the operation effect of micro-turbojet aircraft, it is very important to adjust the parameters of PID controller. The efficiency and accuracy of parameter tuning tests are determined by the platform used. However, existing platforms are limited to single tests, making the process complex and reliant on the experience of testing personnel. In order to optimize the tuning test of micro-turbojet aircraft, this study designed a new aircraft tuning platform based on BP neural network and fuzzy algorithm. The platform focuses on controlling the steering gear and electric cylinder. It adjusts the multidimensional parameters of the aircraft and determines the thrust of each turbojet engine, serving as the basis for evaluating optimization effects. This ensures improved adaptability and robustness of the aircraft. The feasibility of the parameter adjustment platform is verified by simulation. The tracking error peak value is ±0.088, which realizes the goal of multiple parameter adjustment in a single test. The results of this study offer robust support for advancing micro-turbojet technology. They also establish a solid foundation for extending its application into areas such as unmanned aerial vehicles and small-scale commercial aircraft.