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: The present invention relates to a prediction method for stall and surge of an axial compressor based on deep learning. The method comprises the following steps: firstly, preprocessing data with stall and surge of an aeroengine, and partitioning a test data set and a training data set from experimental data. Secondly, constructing an LR branch network module, a WaveNet branch network module and a LR-WaveNet prediction model in sequence. Finally, conducting real-time prediction on the test data: preprocessing test set data in the same manner, and adjusting data dimension according to input requirements of the LR-WaveNet prediction model; giving surge prediction probabilities of all samples by means of the LR-WaveNet prediction model according to time sequence; and giving the probability of surge that data with noise points changes over time by means of the LR-WaveNet prediction model, to test the anti-interference performance of the model.

Abstract: The present invention belongs to the field of aviation compressor control, and relates to an aviation compressor active stabilization control method based on disturbance observation and compensation. Modeling errors and external disturbances of models used in design of a controller are observed, and sub-controllers are individually designed for state variables of interest to compensate for the disturbances, thus to simultaneously solve the problems of rotating stall and surge of an aviation compressor in a variety of complex situations.

Abstract: The present invention provides a spatiotemporal dynamic system soft sensing method for automatically determining a partial differential equation (PDE) structure and belongs to the technical field of soft sensing of neural networks. Firstly, a loss function for training a coupled physics-informed neural network with a recurrent prediction mechanism is constructed to obtain a solution and a driving source which satisfy a PDE used for describing spatiotemporal industrial processes; secondly, differential operator candidates are obtained by an automatic differentiation method, and an appropriate PDE structure is selected from the differential operator candidates to accurately describe the spatiotemporal industrial processes; and finally, the soft sensing result is verified using heat diffuse phenomena and actual vibration processes.

Abstract: A coupled physics-informed neural network for solving displacement distribution of a bounded vibration string under an unknown external driving force is provided. A novel PINN is proposed, called C-PINN, used for solving the displacement distribution of the bounded vibration string under an external driving force with little or even no priori information. It comprises two neural networks: NetU and NetG. NetU is used for approximating satisfying the displacement distribution of the bounded vibration string under study. NetG is used for regularizing u in the NetU to satisfy the displacement distribution of the approximation of NetU. The two networks are integrated into a data-physics-hybrid loss function. In addition, a proposed hierarchical training strategy is used for optimizing the loss function and realizing the coupling of the two networks. Finally, the performance of the C-PINN in solving the displacement distribution of the bounded vibration string under the external driving force is verified.

Abstract: A generative adversarial multi-headed attention neural network self-learning method for aero-engine data reconstruction belongs to the field of end-to-end self-learning of aero-engine missing data. First, the samples are pre-processed, and the machine learning algorithm is used to pre-fill the normalized data first, and the pre-filled information is involved in the network training as part of the training information. Second, a generative adversarial multi-headed attention network model is constructed and the trained sample set is used to train the generative adversarial multi-headed attention network model. Finally, the samples are generated using the trained sample generator G. The method uses the generative adversarial network to better learn the distribution information of the data, and uses parallel convolution and multi-headed attention mechanism to fully exploit the spatial and temporal information among the aero-engine data.

Abstract: A multi-scale hybrid attention mechanism modeling method for aero-engine remaining useful life prediction belongs to the field of health management and prediction technology for aero-engines. Firstly, preprocess the data to obtain the sample, set the RUL label, and obtain the true value of the remaining useful life of the sample. Secondly, a multi-scale hybrid attention mechanism model consisting of position encoding layer, feature extraction layer, and regression prediction layer is constructed. Thirdly, train the model, and make the difference between the predicted value output by the model and the true value of the remaining useful life by minimizing the loss function until it reaches the stop standard. Finally, use the trained model to predict the remaining useful life. The method can achieve full fusion of information from different time steps of a single sample, taking into account the correlation between all samples.

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 coaxial tilt-rotor unmanned aerial vehicle (CTRUAV) and a control method thereof. The CTRUAV includes three rotor modules, five rotors with motors and a control system. The three rotor modules are in an inverted triangle layout. The left and right coaxial tiltable rotor modules in the front of the CTRUAV can rotate around the plane of a fuselage. A rear rotor is installed on the rear fixed-axis rotor module. Two pairs of coaxial rotors are respectively installed on the left and right coaxial tiltable rotor modules. The left and right coaxial tiltable rotor modules include an upper rotor and a lower rotor respectively; the upper rotor and the lower rotor have opposite rotation directions and the same rotation speed during the flight. In the two pairs of coaxial rotors, the rotors on the same layers have opposite rotation directions, and the rotors on different layers have the same rotation directions.

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: 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.