Dalian University of Technology

Yanhua Ma

Ximing Sun

Xian Du

Rui Wang

Zhongxuan Luo

Xin Fan

Lei Wang

Yuhu Wu

Hongyang Zhao

Min Liu

Nan Ding

Weiguo Xia

Zhimin Wang

Tongjian Liu

Ye Wang

Yanlei Gao

Fuxiang Quan

Pan Qin

Xudong Zhao

Xinyue Wang

Ming Zhu

Jinhao Pan

Zhenquan Qin

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.

Prediction method for stall and surge of axial compressor based on deep learning

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.

Intelligent control method for dynamic neural network-based variable cycle engine

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.

Adaptive boosting algorithm-based turbofan engine direct data-driven control method

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.

Interval error observer-based aircraft engine active fault tolerant control method

A bumpless transfer fault tolerant control method for aero-engine under actuator fault is disclosed. For an aero-engine actuator fault, by adopting an undesired oscillation problem produced by an active fault tolerant control method based on a virtual actuator, in order to solve the shortage of the existing control method, a bumpless transfer active fault tolerant control design method for the aero-engine actuator fault is provided, which can guarantee that a control system of the reconfigured aero-engine not only has the same state and output as an original fault-free system without changing the structure and parameters of a controller, to achieve a desired control objective, and that a reconfigured system has a smooth transient state, that is, output parameters such as rotational speed, temperature and pressure do not produce the undesired transient characteristics such as overshoot or oscillation.

Bumpless transfer fault tolerant control method for aero-engine under actuator fault

A design method of aero-engine on-line optimization and multivariable control based on model prediction control realizes aero-engine multivariable control and on-line optimization according to thrust, rotational speed and other needs under the condition of meeting constraints. The first part is a prediction model acquisition layer that continuously establishes a small deviation linear model of an aero-engine near different steady state points based on the actual operating state of the aero-engine in each control cycle and external environment parameters and that supplies model parameters to a controller; and the second part is a control law decision-making layer which is a closed loop structure that consists of a model prediction controller and an external output feedback. The model prediction controller determines the output of the controller at next moment by solving a linear optimization problem according to an engine model in the current state, a control instruction and relevant constraints.

Method of aero-engine on-line optimization and multivariable control based on model prediction

A steady state and transition state multi-variable control method of a turbofan engine based on an active disturbance rejection control theory (ADRC) belongs to the technical field of aero-engine control. Firstly, multiple groups of manipulated variables and controlled variables of a turbofan engine are preliminarily selected, and then the manipulated variables and controlled variables with high correlation are further determined by a correlation analysis method. Each group of control instructions is planned as a tracking trajectory by using a tracking differentiator, and is used as the input of a nonlinear proportional-differential controller together with the current state estimated by the extended state observer to calculate the manipulated variables.

Steady state and transition state multi-variable control method of a turbofan engine based on an active disturbance rejection control theory (ADRC)

The present invention discloses a method for fault diagnosis of the sensors and actuators of an aero-engine based on LFT, and belongs to the field of fault diagnosis of aero-engines. The method comprises: establishing an aero-engine state space model using a combination of a small perturbation method and a linear fitting method; establishing an affine parameter-dependent linear-parameter-varying (LPV) model of the aero-engine based on the model; converting the LPV model of the aero-engine having perturbation signals and sensor and actuator fault signals into a linear fractional transformation (LFT) structure to obtain an synthesis framework of an LPV fault estimator; solving a set of linear matrix inequalities (LMIs) to obtain the solution conditions of the fault estimator; and designing the fault estimator in combination with the LFT structure to realize fault diagnosis of the sensors and actuators of an aero-engine.

Method for fault diagnosis of aero-engine sensor and actuator based on LFT

A stochastic configuration network based turbofan engine health parameter estimation method is disclosed. The stochastic configuration network based turbofan engine health parameter estimation method designed by the present invention combines the model based Kalman filter algorithm and the data-driven based stochastic configuration network, i.e. using the output of the stochastic configuration network as the compensation of the Kalman filter algorithm, so as to take into account the estimated result of the Kalman filter and the estimated result of the stochastic configuration network and improve the estimation accuracy of the original Kalman filter algorithm when the measurable parameters of the turbofan engine are less than the health parameters to be estimated. In addition, the present invention effectively reduces the accuracy loss caused by the poor structure of the neural network through the stochastic configuration network, and improves the generalization ability of the network.

Stochastic configuration network based turbofan engine health parameter estimation method

A Simulink modeling method for a mechanical hydraulic device of an aeroengine fuel regulator is proposed. The Simulink modeling method can implement high precision simulation of a mechanical hydraulic device of an engine fuel conditioning system, and greatly increase the simulation speed as compared with the existing modeling simulation in AMESim; solve the problem of a double-layered nested algebraic loop occurring when the mechanical hydraulic device is modeled in Simulink, and improve the simulation precision of the system. In addition, because of having certain universality, the resolving method for a double-layered nested algebraic loop can be generalized to resolve other types of algebraic loops. Meanwhile, the parameters of the simulation model provided by the present invention can be conveniently modified, and can provide a reference for modeling simulation of mechanical and hydraulic devices of engine fuel conditioning systems of other types.

Simulink modeling method for mechanical hydraulic device of aeroengine fuel regulator

A design method for optimization of a transient control law of the aero-engine is disclosed, and performs the transient schedule optimization for the aero-engine by adopting an SQP algorithm, to realize the design of the transient control law along a constrained boundary condition. The fuel flow rate value is adjusted, other constraints remain unchanged, and the transient control law is designed under different limits. The transient time under each transient control law is calculated by constructing the transient time evaluation function. A lookuptable interpolation table is established by using the calculated transient time and corresponding fuel flow, to realize the fuel flow scheduling under different transient time. The fuel flow obtained by scheduling in the expected time is taken as an acceleration and deceleration control schedule of the closed-loop control of the aero-engine, and the output thereof is taken as the reference instruction of an acceleration process.

Method for optimization of transient control law of aero-engine

A submarine optical positioning beacon system with self-generating capability, which has an array of underwater beacons. When the underwater rover moves to the vicinity of an certain underwater beacon, the underwater beacon's COMS sensor detects the underwater rover's light and then turns on the LED lamp group. The COMS sensor of the underwater rover analyzes the light species of the LED light group and converts it into digital information. The underwater rover analyzes the digital signal to obtain its location. Each underwater beacon has an independent power generation component, which generates power by utilizing ocean current, greatly increasing the working time of the beacon. The LED lamp group gives positional information feedback through the light, which can reduce the system power consumption and increase the system working duration.

Growing community of inventors

Liaoning, China

Yanhua Ma