Abstract: The present invention provides a prediction method for stall and surging of an axial-flow compressor based on a deep autoregressive network. Firstly, selecting and preprocessing surging experimental data of a certain type of aero-engine, and dividing the data into a training set and a test set. Secondly, building and training a deep autoregressive network model based on an attention mechanism, using the finally trained model to conduct real-time prediction on the test set, and giving a model loss and an evaluation index. Finally, using a prediction model to conduct real-time prediction on the test data, and giving a trend of surging probability varying with time in chronological order. The present invention uses the attention mechanism to effectively capture the features of the experimental data and accurately predict the surging probability, which improve the stability and accuracy of prediction, is beneficial to improving the performance of active control of the engine.

Abstract: A design method for an inerter with adaptively adjusted inertia ratio is based on a lead screw-flywheel inerter, which is to change the positions of mass blocks on a flywheel along the radial direction of the flywheel, so as to change of the moment of inertia of the flywheel, and thus to realize adaptive adjustment of the inertia ratio of the inerter. Specifically, the change of angular velocity of the flywheel is caused by the change of an external force load on a lead screw, a centrifugal force on the mass blocks in spring-mass block structures is changed by the angular velocity, and the positions of the mass blocks in the radial direction of the flywheel is determined by the balanced relation of the centrifugal force and a spring restore force, so that the design purpose is achieved.

Abstract: Disclosed are a Nested Named Entity Recognition method based on part-of-speech awareness, system, device and storage medium therefor. The method uses a BiLSTM model to extract a feature of text word data in order to obtain a text word depth feature, and each text word of text to be recognized is initialized into a corresponding graph node, and a text heterogeneous graph of the text to be recognized is constructed according to a preset part-of-speech path, the text word data of the graph nodes is updated by an attention mechanism, and the features of all graph nodes of the text heterogeneous graph are extracted using the BiLSTM model, and a nested named entity recognition result is obtained after decoding and annotating. The present disclosure can recognize ordinary entities and nested entities accurately and effectively, and enhance the performance and advantages of the nested named entity recognition model.

Abstract: The present invention provides an optimization control method for an aero-engine transient state based on reinforcement learning, and belongs to the technical field of aero-engine transient states. The method comprises: adjusting an existing twin-spool turbo-fan engine model as a model for invoking a reinforcement learning algorithm; to simultaneously satisfy high level state space and continuous action output of a real-time model, designing an Actor-Critic network model; designing a deep deterministic policy gradient (DDPG) algorithm based on an Actor-Critic frame, to simultaneously solve the problems of high-dimensional state space and continuous action output; training the model after combining the Actor-Critic frame with the DDPG algorithm; and obtaining the control law of engine acceleration transition from the above training process, and using the method to control an engine acceleration process.

Abstract: The present invention provides an optimization algorithm for automatically determining variational mode decomposition parameters based on bearing vibration signals. First, mode energy is used to reflect bandwidth, a bandwidth optimization sub-model is established to automatically obtain optimal bandwidth parameter ?opt. Secondly, energy loss optimization sub-model is established to avoid under-decomposition. Thirdly, a mode mean position distance optimization sub-model is established to prevent the generation of too much K and avoid the phenomenon of over-decomposition.

Abstract: A steady flow prediction method in a plane cascade based on a generative adversarial network is provided. Firstly, CFD simulation experimental data in the plane cascade are preprocessed, and a test dataset and a training dataset are divided from the simulation experimental data. Then, an Encoding-Forecasting network module, a deep convolutional network module and a generative adversarial network prediction model are constructed successively. Finally, prediction is conducted on test set data: the test set data is preprocessed in the same manner, and data dimensions are adjusted according to input requirements of a saved optimal prediction model; and flow field images in the plane cascade at an inlet attack angle of 10° are obtained through the prediction model. The present invention can effectively avoid the problem of limited measurement range of sensors in an axial flow compressor, and the prediction result is highly consistent with the calculation result of CFD.

Abstract: An aero-engine surge active control system based on fuzzy controller switching is provided. The present invention selects a basic controller with the most appropriate current state for switching control according to the operating state of a compressor based on the principle of fuzzy switching, and can realize large-range, adaptive and performance-optimized surge active control. Controllers designed by the present invention realize large-range surge active control through fuzzy switching, so that the effective operating ranges of the controllers are expanded and the reliability of the controllers is improved. The designed controllers can be applied to the active control of surge caused by various causes, so that the adaptability of the controllers is improved and is closer to the actual operating condition of the engine. Some optimization indexes are added in the design process of the controllers, which can realize optimal control under corresponding optimization objectives.

Abstract: An intelligent control method for a dynamic neural network-based variable cycle engine is provided. By adding a grey relation analysis method-based structure adjustment algorithm to the neural network training algorithm, the neural network structure is adjusted, a dynamic neural network controller is constructed, and thus the intelligent control of the variable cycle engine is realized. A dynamic neural network is trained through the grey relation analysis method-based network structure adjustment algorithm designed by the present invention, and an intelligent controller of the dynamic neural network-based variable cycle engine is constructed. Thus, the problem of coupling between nonlinear multiple variables caused by the increase of control variables of the variable cycle engine and the problem that the traditional control method relies too much on model accuracy are effectively solved.

Abstract: The present invention belongs to the technical field of aviation electrics and electric power, and provides an aircraft grid phase angle tracker based on nonlinear active disturbance rejection, which is used to estimate the grid phase angle on AC side of an aircraft grid. A embedded generator in the aircraft grid is arranged inside a compressor of an aviation gas turbine engine, and the embedded generator is directly coupled with the aviation gas turbine engine so that the AC frequency of the embedded generator varies with the speed of the aviation gas turbine engine. The present invention applies the nonlinear active disturbance rejection technology to the phase angle tracking of the more electric aircraft grid, is simple in operation and high in accuracy, and can realize high-accuracy tracking of the grid phase angle. The method has certain extensibility and can be extended to other fields.

Abstract: The present invention belongs to the technical field of control of aero-engines, and proposes an adaptive boosting algorithm-based turbofan engine direct data-driven control method. First, a turbofan engine controller is designed based on the Least Squares Support Vector Machine (LSSVM) algorithm, and further, the weight of a training sample is changed by an adaptive boosting algorithm so as to construct a turbofan engine direct data-driven controller combining a plurality of basic learners into strong learners. Compared with the previous solution only adopting LS SVM, the present invention enhances the control precision, improves the generalization ability of the algorithm, and effectively solves the problem of sparsity of samples by the adaptive boosting method. By the adaptive boosting algorithm-based turbofan engine direct data-driven control method designed by the present invention.

Abstract: A pre-alarming method for rotary stall of compressors based on a temporal dilated convolutional neural network includes firstly, preprocessing dynamic pressure data of an aero-engine, and dividing a test dataset and a training dataset from experimental data; secondly, constructing a temporal convolutional network module, a Resnet-v network module and a temporal dilated convolutional network prediction model in sequence, and saving an optimal prediction model.

Abstract: A finite time speed control method for a permanent magnet synchronous motor (PMSM) based on a fast integral terminal sliding mode and disturbance estimation comprises: firstly, determining a mathematical model of a speed loop of the PMSM under the influence of system parameters uncertainty and unknown load torque; secondly, designing an improved fast integral terminal sliding surface on the basis of the idea of terminal sliding mode control; then, proposing a disturbance estimation method based on an adaptive fuzzy system with respect to the disturbance in a PMSM system; designing a PMSM speed controller on this basis; and finally, completing the concrete implementation of the whole technical solution. The present invention designs the fast integral terminal sliding surface and a sliding mode control law to ensure that a motor speed tracking error converges to zero within finite time and enhances the rapidity of a PMSM speed regulating system.

Abstract: A speed control method for a permanent magnet synchronous motor considering current saturation and disturbance suppression aims to effectively ensure that a current of the motor is always within a given range to avoid the problem of control performance reduction caused by the fact that the current gets into a saturation state, ensure the safety of a system, do not need to use unavailable state variables such as motor acceleration and the like, effectively estimate and compensate disturbances including parameters uncertainty and unknown load torque disturbance existing in a permanent magnet synchronous motor system, and rapidly and accurately control a speed of the motor finally. There is no need to configure a plurality of sensors in practical industrial application, so system building costs can be reduced on the one hand, and the stability of the system can be improved on the other hand.

Abstract: A two-dimensionality detection method for industrial control system attacks: collecting data; transmitting the data to a PLC and an embedded attack detection system; uploading, by the PLC, received data to an SCADA system; transmitting, by the SCADA system, the data to the embedded attack detection system after classifying and counting the data; before starting detection, directly reading, by the embedded attack detection system, the data measured by sensors; refining data association relationships and probability distribution characteristics of the sensors of normal operation to complete storage of health data model; after starting detection, in first dimensionality, comparing the data collected directly by the sensors with statistical data of the SCADA system to judge the attacked condition of the SCADA system, and in second dimensionality, comparing the characteristics of the data collected directly by the sensors and counted online with the health data model to judge the attacked condition of the sensors.

Abstract: The present invention provides an interval error observer-based aircraft engine active fault tolerant control method, and belongs to the technical field of aircraft control. The method comprises: tracking the state and the output of a reference model of an aircraft engine through an error feedback controller; compensating a control system of the aircraft engine having a disturbance signal and actuator and sensor faults through a virtual sensor and a virtual actuator; observing an error between a system with fault of the aircraft engine and the reference model through an interval error observer, and feeding back the error to the error feedback controller; and finally, using a difference between the output of the reference model of the system with fault and the output of the virtual actuator as a control signal to realize active fault tolerant control of the aircraft engine.

Abstract: A two-dimensionality detection method for industrial control system attacks: collecting data; transmitting the data to a PLC and an embedded attack detection system; uploading, by the PLC, received data to an SCADA system; transmitting, by the SCADA system, the data to the embedded attack detection system after classifying and counting the data; before starting detection, directly reading, by the embedded attack detection system, the data measured by sensors; refining data association relationships and probability distribution characteristics of the sensors of normal operation to complete storage of health data model; after starting detection, in first dimensionality, comparing the data collected directly by the sensors with statistical data of the SCADA system to judge the attacked condition of the SCADA system, and in second dimensionality, comparing the characteristics of the data collected directly by the sensors and counted online with the health data model to judge the attacked condition of the sensors.

Abstract: A robust position control method for a permanent magnet synchronous motor considering current limitation is provided. The method fully considers the influence of current limitation on a closed loop system in controller design, stability analysis and other theoretical analysis phase, can effectively overcome the influence of system disturbances including system parameters uncertainty and unknown load torque, and finally realize a control objective of accurate tracking of the motor position. More importantly, the technology is a continuous control method which can overcome the inherent chattering problem while having strong robustness of sliding mode control. Meanwhile, a controller designed by the present invention also has the advantages of simple structure, etc. The technical solution proposed by the present invention has wide practical application prospect due to the characteristics of excellent anti-disturbance capability, and simple and feasible structure.

Abstract: A robust position control method for a permanent magnet synchronous motor considering current limitation is provided. The method fully considers the influence of current limitation on a closed loop system in controller design, stability analysis and other theoretical analysis phase, can effectively overcome the influence of system disturbances including system parameters uncertainty and unknown load torque, and finally realize a control objective of accurate tracking of the motor position. More importantly, the technology is a continuous control method which can overcome the inherent chattering problem while having strong robustness of sliding mode control. Meanwhile, a controller designed by the present invention also has the advantages of simple structure, etc. The technical solution proposed by the present invention has wide practical application prospect due to the characteristics of excellent anti-disturbance capability, and simple and feasible structure.

Abstract: The present invention belongs to the technical field of aviation electrics and electric power, and provides an aircraft grid phase angle tracker based on nonlinear active disturbance rejection, which is used to estimate the grid phase angle on AC side of an aircraft grid. A embedded generator in the aircraft grid is arranged inside a compressor of an aviation gas turbine engine, and the embedded generator is directly coupled with the aviation gas turbine engine so that the AC frequency of the embedded generator varies with the speed of the aviation gas turbine engine. The present invention applies the nonlinear active disturbance rejection technology to the phase angle tracking of the more electric aircraft grid, is simple in operation and high in accuracy, and can realize high-accuracy tracking of the grid phase angle. The method has certain extensibility and can be extended to other fields.

Abstract: A design method for undisturbed switching of linear controllers is provided for solving the problem of sudden system response change or an unstable control circuit caused by switching of multiple linear controllers, in which the linear controllers include proportional-integral-derivative (PID), linear-quadratic-Gaussian (LQG), linear active disturbance rejection control (LADRC), H?, model reference adaptive control (MRAC), and open-loop controllers. Firstly, differentials tbr outputs of the controllers are found, then through controller decision, a controller is selected to be connected to a closed-loop control circuit, and a differential term of the controller connected to the closed-loop control circuit is integrated through a common integrator, thereby ensuring smooth controller switching. The design method has a simple structure and good versatility, is operable, and can be easily applied to various actual control systems without requiring parameter adjustment.