Aero-engine Control System Research Articles

Bumpless transfer control for switched time-delay systems with application to aero-engine control

For switched time-delay systems, the bumpless transfer control issue is investigated by utilizing the multiple piecewise Lyapunov–Krasovskii function approach. First, a time-delay-dependent bumpless transfer concept is put forward to depict the transient performance of switched time-delay systems, which restricts the bumps in the amplitude of the present state and the time-delay state, simultaneously. Second, a state-dependent switching signal with a prescribed time is designed to avoid switching frequently. A time-varying switching controller is developed to decrease control bumps. Third, under the premise of achieving asymptotic stability, a solvability condition ensuring a satisfactory bumpless transfer performance is derived through relaxing the traditional bumpless transfer condition. Finally, an instance of the aero-engine control system is used for exhibiting the validity of the presented method.

Two-Stage Hyperelliptic Kalman Filter-Based Hybrid Fault Observer for Aeroengine Actuator under Multi-Source Uncertainty

An aeroengine faces multi-source uncertainty consisting of aeroengine epistemic uncertainty and the control system stochastic uncertainty during operation. This paper investigates actuator fault estimation under multi-source uncertainty to enhance the fault diagnosis capability of aero-engine control systems in complex environments. With the polynomial chaos expansion-based discrete stochastic model quantification, the optimal filter under multi-source uncertainty, the Hyperelliptic Kalman Filter, is proposed. Meanwhile, by treating actuator fault as unknown input, the Two-stage Hyperelliptic Kalman Filter (TSHeKF) is also proposed to achieve optimal fault estimation under multi-source uncertainty. However, considering that the biases of the model are often fixed for the individual, the TSHeKF-based fault estimation is robust and leads to inevitable conservativeness. By adding the additional estimation of the unknown deviation in state function caused by probabilistic system parameters, the hybrid fault observer (HFO) is proposed based on the TSHeKF and realizes conservativeness-reduced estimation for actuator fault under multi-source uncertainty. Numerical simulations show the effectiveness and optimality of the proposed HFO in state estimation, output prediction, and fault estimation for both single and multi-fault modes, when considering multi-source uncertainty. Furthermore, Monte Carlo experiments have demonstrated that the HFO-based optimal fault estimation is less conservative and more accurate than the Two-stage Kalman Filter and TSHeKF, providing better safety and more reliable aeroengine operation assurance.

Mathematical modeling and time-frequency characterization of aero-engine actuators

The actuator is an essential part of the aero-engine control system, and its performance directly determines the efficiency of the whole system. The mathematical model of the aero-engine actuators, including electro-hydraulic servo valves, hydraulic cylinders, and actuators, is modeled and the time-frequency characteristics are analyzed using the Simulink module. Then, the system response is simulated and its time-frequency characteristics are analyzed. The simulation results show that the simulation error of the established actuator model is within the allowable range. The displacement response and the servo valve flow response fluctuate slightly near the steady state value, and the error value is only 0.857% at the maximum fluctuation value, and the system still works normally when a dynamic load is added to the system.

Fault-Tolerant Control Based on Set-Membership Estimation for Linear Discrete-Time Systems With Parameter Uncertainties and Application to Aero-Engine Control System

Fault-Tolerant Control Based on Set-Membership Estimation for Linear Discrete-Time Systems With Parameter Uncertainties and Application to Aero-Engine Control System

Design and implementation for the state time-delay and input saturation compensator of gas turbine aero-engine control system

In order to obtain desired steady-state and transient performance of a gas turbine aero-engine during its operation, a usual way is to control the engine to operate in a manner close to its physical limits. The thermodynamic performance of a gas turbine aero-engine varies greatly within a wide working envelope, affected by factors such as engine performance degradation, actuator response lag and combustion time-delay. When the engine works in extreme conditions, for example rapid acceleration, some phenomena such as over-speed, over-temperature and over-pressure may occur. For this reason, various restrictions are often set in the engine control system to ensure safety during its operation, which are often realized by the form of saturation. In this paper, a compensation structure for gas turbine aero-engine state time-delay and input saturation is proposed. At first, the problems frequently occurred in the gas turbine aero-engine control system are investigated. And the gas turbine aero-engine control model is derived based on a thermodynamic non-linear model and its linearization model is established, together with its designed compensation structure. Differing from the existing technologies, a combination of dynamic system input saturation feedback and static state time-delay feedback is adopted in the proposed model. Then, the saturated nonlinearity and time-delay characteristic are introduced as convex combinations in the stability analysis. The local stability conditions of the closed-loop system are obtained by using Lyapunov–Krasovskii functionals and linear matrix inequalities (LMIs). Finally, hardware-in-loop (HIL) experiments are conducted to verify the feasibility of the proposed compensation method.

Bumpless Transfer Control for Switched Systems via a Dynamic Feedback and a Bump-Dependent Switching Law.

This article investigates the bumpless transfer (BT) control problem of switched systems with unmeasured states. Note that the control input signal of the switched system jumps when switching occurs, which may adversely affect the performance of switched systems. By introducing a modified state observer and an auxiliary continuous control signal, a novel description of the BT performance for the system is presented. Moreover, a compensation-based dynamic BT controller and a bump-dependent switching law are designed to solve the BT control problem. As a result, the developed control method ensures the stability of the closed-loop system while maintaining BT performance. The validation of the results can be provided by applying the proposed BT control method to an example of an aero-engine control system.

Physics-Guided Neural Network Model for Aeroengine Control System Sensor Fault Diagnosis under Dynamic Conditions

Sensor health assessments are of great importance for accurately understanding the health of an aeroengine, supporting maintenance decisions, and ensuring flight safety. This study proposes an intelligent framework based on a physically guided neural network (PGNN) and convolutional neural network (CNN) to diagnose sensor faults under dynamic conditions. The strength of the approach is that it integrates information from physics-based performance models and deep learning models. In addition, it has the structure of prediction–residual–generation-fault classification that effectively decouples the interaction between sensor faults and system state changes. First, a PGNN generates the engine’s non-linear dynamic prediction output because the PGNN has the advantage of being able to handle temporal information from the long short-term memory (LSTM) network. We use a cross-physics–data fusion scheme as the prediction strategy to explore the hidden information of the physical model output and sensor measurement data. A novel loss function that considers physical discipline is also proposed to overcome the performance limitations of traditional data-driven models because of their physically inconsistent representations. Then, the predicted values of the PGNN are compared with the sensor measurements to obtain a residual signal. Finally, a convolutional neural network (CNN) is used to classify faults for residual signals and deliver diagnostic results. Furthermore, the feasibility of the proposed framework is demonstrated on an engine sensor fault dataset. The experimental results show that the proposed method outperforms the pure data-driven approach, with the predicted RMSE being reduced from 1.6731 to 0.9897 and the diagnostic accuracy reaching 95.9048%, thereby confirming its superior performance.

Data-driven nonlinear MIMO modeling for turbofan aeroengine control system of autonomous aircraft

The mathematical modeling problem of a general turbofan aeroengine control system used in autonomous aircraft is investigated in this paper. Unlike the thermodynamics or physical mechanism-based modeling methods in the existing literature, a pure data-driven modeling approach is presented with high accuracy via online and offline data only. This is achieved by using the nonlinear autoregressive neural network with exogenous inputs (NARX) neural network. In comparison with the existing NARX-based MIMO modeling methods, the series–parallel structure and the parallel structure are integrated for the neural network training. Faster convergence and stability as well as higher modeling accuracy are thus achieved. Another feature of this approach is that it is independent of all the components’ dynamics in the turbofan aeroengine. Simulation results are given to verify the effectiveness of the proposed modeling approach. This is further validated by experimental testing with the method applied to mini turbofan aeroengine testbed.

A Decentralized LQR Output Feedback Control for Aero-Engines

Aero-engine control systems generally adopt centralized or distributed control schemes, in which all or most of the tasks of the control system are mapped to a specific processor for processing. The performance and reliability of this processor have a significant impact on the control system. Based on the aero-engine distributed control system (DCS), we propose a decentralized controller scheme. The characteristic of this scheme is that a network composed of a group of nodes acts as the controller of the system, so that there is no core control processor in the system, and the computation is distributed throughout the entire network. An LQR output feedback control is constructed using system input and output, and the control tasks executed on each node in the decentralized controller are obtained. The constructed LQR output feedback is equivalent to the optimal LQR state feedback. The primal-dual principle is used to tune the parameters of each decentralized controller. The parameter tuning algorithm is simple to calculate, making it conducive for engineering applications. Finally, the proposed scheme was verified by simulation. The simulation results show that a high-precision feedback gain matrix can be obtained with a maximum of eight iterations. The parameter tuning algorithm proposed in this paper converges quickly during the calculation process, and the constructed output feedback scheme achieves equivalent performance to the state feedback scheme, demonstrating the effectiveness of the design scheme proposed in this paper.

Aero-Engine Modeling and Control Method with Model-Based Deep Reinforcement Learning

Due to the strong representation ability and capability of learning from data measurements, deep reinforcement learning has emerged as a powerful control method, especially for nonlinear systems, such as the aero-engine control system. In this paper, a novel application of deep reinforcement learning (DRL) is presented for aero-engine control. In addition, transition dynamic characteristic information of the aero-engine is extracted from the replay buffer of deep reinforcement learning to train a neural-network dynamic prediction model for the aero-engine. In turn, the dynamic prediction model is used to improve the learning efficiency of reinforcement learning. The practical applicability of the proposed control system is demonstrated by the numerical simulations. Compared with the traditional control system, this novel aero-engine control system has faster response speed, stronger self-learning ability, and avoids the complicated manual parameter adjustment without sacrificing the control performance. Moreover, the dynamic prediction model has satisfactory prediction accuracy, and the model-based method can achieve higher learning efficiency than the model-free method.

An Optimal Simulation Configuration Method for Real-Time Simulation Based on Simulation Requirements

An optimal configuration method of simulation parameters, including the solver and step size of each sub-model, is proposed to realize real-time virtual simulation. The content of the presented study is threefold: firstly, the simulation requirements of three simulation modes, including Model-Exchange, Co-Simulation, and Hybrid Co-Simulation, are analyzed; secondly, the mathematical descriptions of the three simulation configuration problems are constructed, which are classified as an inequality problem and two assignment problems, where the assignment problems are decoupled; finally, an optimal simulation configuration procedure is proposed, where the analysis method and corresponding design methods for configured simulation parameters of each sub-model are given. The configuration method is applied to configure the simulation parameters of an aeroengine control system model with 6 separate components, and the simulation results verify the effectiveness of the proposed method. Specifically, the feasibility analysis results of the sub-models reveal that 4 of them meet the requirements while 2 of them are numerically unstable, which are consistent with the verification results. Meanwhile, after redesigning the infeasible sub-models using the proposed design methods, all the sub-models can meet their simulation requirements. In addition, an optimal configuration of the simulation parameters as well as several comparative configurations are provided, and the simulation results show that the time consumption of the optimal configuration is minimal and it can achieve real-time performance.

A novel compound fault-tolerant method based on online sequential extreme learning machine with cycle reservoir for turbofan engine direct thrust control

Sensors are the primary information source of the aeroengine control system, their measurement accuracy is closely related to whether the engine can operate safely and efficiently. Aiming at the direct thrust control system of aeroengines, this paper proposes an intelligent prediction algorithm combining feedforward and recurrent networks and a fault-tolerant control strategy combining analytical redundancy and controller switching. First, the cycle reservoir with regular jumps is introduced into the online sequential extreme learning machine. The CR-OSELM is developed, which maps the inputs from the low-dimensional space to the high-dimensional space. Then the CR-OSELM is adopted to build the sensor measurement prediction module, and identify and reconstruct the sensor bias and drift fault. Furthermore, in allusion to the rotor speed sensor and temperature/pressure sensor faults, analytic redundancy and controller switching strategies are designed respectively, realizing the fault-tolerant control of thrust feedback. The main contribution of this paper is to come up with the CR-OSELM algorithm with the ability to save historical input information, which overcomes the limitations of traditional feedforward neural networks in identifying time series. And the compound fault-tolerant control scheme is put forward based on the fault characteristics and their impact on the direct thrust control system, which can deal with different types of sensor faults. Simulations are performed on some benchmark datasets and a turbofan engine model to investigate the time series prediction accuracy, sensor fault diagnosis, and thrust fault-tolerant control effect. The results show that the system can detect various faults rapidly, realize the analytic redundancy with high accuracy, reduce the influence of sensor faults and restore the engine performance.

Experimental Study on the Coupling Mechanism of Sensors under a Strong Electromagnetic Pulse.

This paper presents a study on the electromagnetic effects of a main fuel control system under the action of a strong electromagnetic pulse. It assesses the electromagnetic sensitivity of a speed sensor and a linear variable differential transformer (LVDT) sensor. This assessment focuses on the control system’s electromagnetic effects and the sensors’ coupling signals. The effects and signals were determined using radiation and pulse current injection tests. Analysis of the signal at the system power port shows that it is the same as those for the two test methods. Based on analysis of the working mechanism and terminal signals of the sensors, the electromagnetic sensitivity of the system is different under the different electromagnetic pulse conditions. The electromagnetic sensitivity characteristics of the sensors were verified, and the electromagnetic effects of the main fuel regulation control system were analyzed. Meanwhile, the degree of sensor coupling mechanism caused by the electromagnetic coupling effects of the main fuel regulation control system under strong electromagnetic pulses were studied. These findings have clear practical implications for electromagnetic pulse protection of aero-engine control systems.

Event-Triggered Antidisturbance Control Design for Aeroengine Systems via Switched Models

In this paper, an event-triggered antidisturbance (ETAD) control scheme is presented for a category of aeroengine control systems (AECSs) involved in multiple disturbances. First, an AECS in this category is described by a switched model at a series of steady-state points. Then, a dynamic event-triggered mechanism is introduced by constructing the ET condition based on the control input directly, saving the signal transmission resource from the controller to the AECS. Next, a switching law with sampling is developed, economizing the signal transmission resource from the switching law to the AECS. Furthermore, based on the ET mechanism and switching law, an antidisturbance controller is designed to compensate for or suppress the influence of the multiple disturbances on the AECS. Also, under the ETAD control approach, relative theoretical conditions are established to save the signal transmission resource and to suppress or compensate multiple disturbances. Finally, using a hardware-in-loop platform with a switched aeroengine model inserted, a simulation is implemented to validate how the established ETAD control strategy is effectively applied to the AECS.

A State Space Modeling Method for Aero-Engine Based on AFOS-ELM

State space models (SSMs) are important for multi-variable performance analysis and controller design of aero-engines. In order to solve the problems of the traditional state space modeling methods that rely on component-level models (CLMs) and cannot be carried out in real time, an aero-engine state space modeling method based on adaptive forgetting factor online sequential extreme learning machine (AFOS-ELM) is proposed in this paper. The structure of the extreme learning machine (ELM) is determined according to the form of the state space model, and the inverse-free ELM algorithm is used to automatically select the appropriate number of hidden nodes to improve the efficiency of offline initialization. The focus of the ELM on current operation performance is enhanced by the adaptive renewed forgetting factor, which reduces the impact of aero-engine history and deviated data on the current output and improves the accuracy of the model. Then, according to the analytical equation of the ELM model, the state space model of an aero-engine at each sampling time is obtained by using the partial derivative method. The simulation results based on engine test data show that the real-time performance and accuracy of the state space model established online in this paper can meet the needs of aero-engine control system requirement.

A method of multiple model adaptive control of affine systems and its application to aero-engines

This paper investigates the multiple model adaptive control problem of affine systems with unknown parameters. Firstly, an adaptive controller with resettable parameters and an adaptive law with projection function are designed to ensure the asymptotic tracking for the reference system and the boundedness of parameters. Secondly, a transformation of system is given to enable a finite-time parameter estimator to calculate the uncertain parameters in the system matrix and the affine item simultaneously. Then, a novel performance index to describe the error between the controlled plant and the identification model is given to orchestrate switchings among identification models aiming to choose the best one. Next, the sufficient condition of the asymptotic convergence for the system error is given. Finally, all designs are evaluated in a hardware-in-the-loop simulation platform of an aero-engine control system and compared with three other methods, the effectiveness and superiority are verified.

Fault Detection of Aero-Engine Sensor Based on Inception-CNN

The aero-engine system is complex, and the working environment is harsh. As the fundamental component of the aero-engine control system, the sensor must monitor its health status. Traditional sensor fault detection algorithms often have many parameters, complex architecture, and low detection accuracy. Aiming at this problem, a convolutional neural network (CNN) whose basic unit is an inception block composed of convolution kernels of different sizes in parallel is proposed. The network fully extracts redundant analytical information between sensors through different size convolution kernels and uses it for aero-engine sensor fault detection. On the sensor failure dataset generated by the Monte Carlo simulation method, the detection accuracy of Inception-CNN is 95.41%, which improves the prediction accuracy by 17.27% and 12.69% compared with the best-performing non-neural network algorithm and simple BP neural networks tested in the paper, respectively. In addition, the method simplifies the traditional fault detection unit composed of multiple fusion algorithms into one detection algorithm, which reduces the complexity of the algorithm. Finally, the effectiveness and feasibility of the method are verified in two aspects of the typical sensor fault detection effect and fault detection and isolation process.

An Online Data-Driven LPV Modeling Method for Turbo-Shaft Engines

The linear parameter-varying (LPV) model is widely used in aero engine control system design. The conventional local modeling method is inaccurate and inefficient in the full flying envelope. Hence, a novel online data-driven LPV modeling method based on the online sequential extreme learning machine (OS-ELM) with an additional multiplying layer (MLOS-ELM) was proposed. An extra multiplying layer was inserted between the hidden layer and the output layer, where the hidden layer outputs were multiplied by the input variables and state variables of the LPV model. Additionally, the input layer was set to the LPV model’s scheduling parameter. With the multiplying layer added, the state space equation matrices of the LPV model could be easily calculated using online gathered data. Simulation results showed that the outputs of the MLOS-ELM matched that of the component level model of a turbo-shaft engine precisely. The maximum approximation error was less than 0.18%. The predictive outputs of the proposed online data-driven LPV model after five samples also matched that of the component level model well, and the maximum predictive error within a large flight envelope was less than 1.1% with measurement noise considered. Thus, the efficiency and accuracy of the proposed method were validated.

In-the-Loop Simulation Experiment of Aero-Engine Fault-Tolerant Control Technology

Aeroengines are prone to failure due to their large range of working envelopes and bad working environments. Fault diagnosis and a fault-tolerant control strategy for aeroengines and control systems are important means to improve the reliability of aeroengine. In this article, the turbofan engine is taken as the research object, and the fault diagnosis and fault-tolerant control of an aeroengine control system are studied. First, based on the principle of component-level modeling and the algorithm of the extended Kalman filter, an adaptive turbofan model is established, and the adaptive effect of the model in the range of the full envelopment is verified by digital simulation. Next, based on the analytical redundancy provided by the adaptive model, sensor fault diagnosis and fault-tolerant control are studied. The low-voltage speed closed-loop control and EPR closed-loop control are designed, and the sensor fault-tolerant control based on analytic redundancy and the switching control rate is studied. The simulation results show that the filter based on the adaptive model can accurately locate and diagnose the sensor faults, and the sensor fault-tolerance based on the analytic redundancy and switching control rate can be effective fault tolerance for the sensor faults. Finally, as a hardware platform, this article selects MC203 VxWorks as an embedded system, the adaptive model for a turbofan engine as the research object, and has carried on the fault diagnosis and fault-tolerant control in the loop simulation experiment research; the experimental results show that the adaptive model can provide accurate analytical redundancy, and the real-time and fault tolerance of sensor fault effect is better.

Sensor Fault Diagnosis of Aero Engine Control System Based on Honey Badger Optimizer

Traditional redundancy detection methods are challenging to achieve fault detection due to high modeling complexity. Aiming at this problem, a data-driven engine sensor fault diagnosis method based on data is established. This paper proposes an intelligent fault diagnosis method based on meta-heuristic optimization, which uses honey badger optimization for feature selection to reduce redundancy information and improve classification accuracy in the problem of aero-engine sensor fault diagnosis. The accuracy of the proposed algorithm is significantly better than the traditional method.