In-service commercial aero-engine controls its thrust by controlling parameters such as speed/pressure ratio (Nf/EPR) characterized as measurable and indirectly related to the thrust. However, the mapping relationship between the Nf/EPR and the thrust would have unknown and nonlinear changes with the engine performance degrades, naturally causing it difficult to obtain the desired thrust by primarily designed Nf/EPR control and resulting in task degradation. Therefore, this paper proposes an improved double closed-loop self-adaptive performance recovery control method to yield the engine to maintain almost consistent performance in the whole life cycle. Firstly, the inner control loop of speed is designed based on the MPC algorithm to obtain the optimal fuel output through receding horizon optimization to improve the control performance. Secondly, the speed command from the inner control loop of the degraded engine is compensated by adding an outer control loop of thrust to maintain the desired thrust. In this control method, an improved high-precision parameter estimation method is present to estimate unmeasurable engine performance parameters. A steady-state identification algorithm therein is designed to judge the engine state to deal with the problem that the estimator might diverge due to the engine's strongly nonlinear performance changes under the circumstances of high dynamic operation. Finally, the simulation results show that, compared with the traditional method, the proposed method achieves the estimator's stable work, improves the control performance by 25.8%, and maintains the desired thrust in case of engine degradation.

Trajectory planning of multiple unmanned aerial vehicles (UAVs) is the basis for them to form the formation flight. By considering trajectory planning of multiple UAVs in formation flight in three-dimensional space, a trajectory planning method in four-dimensional space-time is proposed which, firstly, according to the formation configuration, adopts the Hungarian algorithm to optimize the formation task allocation. Based on that, by considering the flight safety of UAVs in formation, a hierarchical decomposition algorithm in four-dimensional space-time is innovatively put forward with spatial positions and time constraints both considered. It is applied to trajectory planning and automatic obstacle avoidance under the condition of no communication available between UAVs in the formation. The simulation results illustrated that the proposed method is effective in cooperative trajectory planning and automatic obstacle avoidance in advance for multiple UAVs. Meanwhile, it has been tested in a Swarm Unmanned Aerial System project and boasts quite significant value in engineering applications.

This paper presents an effective method for measuring oil debris with high confidence to ensure the wear monitoring of aero-engines, which suffers from severe noise interference, weak signal characteristics, and false detection. First, an improved variational mode decomposition algorithm is proposed, which combines wavelet transform and interval threshold processing to suppress the complex noise interference on the signal. Then, a long-short-term memory neural network with deep scattering spectrum preprocessing is used to identify the signal characteristics under the multi-resolution analysis framework. The optimal hyperparameters are automatically configured using Bayesian optimization to solve the problem of weak, distorted, and hard-to-extract signal characteristics. Finally, a detection algorithm based on multi-window fusion judgment is applied to improve the confidence of the detection process, reduce the false detection and false alarm rate, and calculate the debris size information according to the sensor principle. The experimental results show that the proposed method can extract debris signals from noise with a signal-to-noise ratio improvement of more than 9 dB, achieve a high recognition accuracy of 99.76% with a missed detection rate of 0.24%, and output size information of debris to meet the need for aero-engine oil debris measurement.

In order to improve the reliability and real-time of the control system of aero-engine, an intelligent fault-tolerant control system based on the online sequential extreme learning machine (OS-ELM) is proposed against the sensor faults. This system can realize the online fault diagnosis and signal reconstruction without a system model. And while considering the traditional PID control robustness and poor anti-interference ability and other shortcomings, an improved global fast non-singular terminal sliding mode control method is used to obtain better control effects, effectively solve the uncertainty problem in aero-engine, and give full play to aero-engine performance. To verify the feasibility and effectiveness of this system based on the above method, a turbofan engine is taken as the research object and semi-physical simulation experiments on fault-tolerant control are conducted on a semi-physical simulation test platform. Results show that the controller of this system can safely and reliably control the aero-engine without losing its control performance under the circumstance that there are faults in engine sensors. The purpose of fault-tolerant control is reached.