The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To addre...The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To address the versatile thrust demand under complex dynamic characteristics of the adaptive cycle engine,this paper proposes a direct thrust estimation and control method based on the Model-Free Adaptive Control(MFAC)algorithm.First,an improved Sliding Mode Control-MFAC(SMC-MFAC)algorithm has been developed by introducing a sliding mode variable structure into the standard Full Format Dynamic Linearization-MFAC(FFDL-MFAC)and designing self-adaptive weight coefficients.Then a trivariate double-loop direct thrust control structure with a controller-based thrust estimator and an outer command compensation loop has been established.Through thrust feedback and command correction,accurate control under multi-mode and operation conditions is achieved.The main contribution of this paper is the improved algorithm that combines the tracking capability of the MFAC and the robustness of the SMC,thus enhancing the dynamic performance.Considering the requirements of the online thrust feedback,the designed MFAC-based thrust estimator significantly speeds up the calculation.Additionally,the proposed command correction module can achieve the adaptive thrust control without affecting the operation of the inner loop.Simulations and Hardware-in-Loop(HIL)experiments have been performed on an adaptive cycle engine component-level model to investigate the estimation and control effect under different modes and health conditions.The results demonstrate that both the thrust estimation precision and operation speed are significantly improved compared with Extended Kalman Filter(EKF).Furthermore,the system can accelerate the response of the controlled plant,reduce the overshoot,and realize the thrust recovery within the safety range when the engine encounters the degradation.展开更多
The acceleration and mode transition performance are two significant performances of Adaptive Cycle Engine(ACE).However,separating the processes of acceleration and mode transition will slow down the response speed of...The acceleration and mode transition performance are two significant performances of Adaptive Cycle Engine(ACE).However,separating the processes of acceleration and mode transition will slow down the response speed of thrust.Therefore,this paper proposes a multi-mode acceleration optimization control method that simultaneously performs ACE acceleration and mode transition.Firstly,an ACE component model with inlet flow characteristics was established,and the performance before and after mode transition were analyzed.Secondly,the principle of ACE acceleration optimization was analyzed,and the Front Variable Area Bypass Injector(FVABI)and Mode Selection Valve(MSV)were adopted in the acceleration process.Finally,based on the Sequential Quadratic Programming(SQP)algorithm,considering the degradation effects of engine components,we optimize the acceleration control plan for fuel and variable geometry mechanisms.The simulation results show that at the subsonic cruise point,the ACE multi-mode acceleration optimization control method can shorten the acceleration time from idle to middle state by 30.33%,and accelerate the thrust response speed by 33.72%.When the compressor flow rate of ACE deteriorates by 2% and the high-pressure turbine efficiency deteriorates by 4%,the adaptive acceleration control plan increases the high-pressure speed by 2.13% and thrust by about 6.82%;within the flight envelope,the acceleration time is reduced by more than 25%,and the thrust response speed is increased by more than 20%.展开更多
The design of a turbofan rotor speed control system, using model reference adaptive control（MRAC） method with input and output measurements, is discussed for the purpose of practical application. The nonlinear compe...The design of a turbofan rotor speed control system, using model reference adaptive control（MRAC） method with input and output measurements, is discussed for the purpose of practical application. The nonlinear compensator based on functional link neural network is used to deal with the engine nonlinearity and the hardware-in-loop simulation is also developed. The results show that the nonlinear MRAC controller has the adequate performance of compensating and adapting nonlinearity arising from the change of engine state or working environment. Such feature demonstrates potential practical applications of MRAC for aeroengine control system.展开更多
As a novel aero-engine concept,adaptive cycle aero-engines（ACEs） are attracting wide attention in the international aviation industry due to their potential superior task adaptability along a wide flight regime.Howe...As a novel aero-engine concept,adaptive cycle aero-engines（ACEs） are attracting wide attention in the international aviation industry due to their potential superior task adaptability along a wide flight regime.However,this superior task adaptability can only be demonstrated through proper combined engine control schedule design.It has resulted in an urgent need to investigate the effect of each variable geometry modulation on engine performance and stability.Thus,the aim of this paper is to predict and discuss the effect of each variable geometry modulation on the matching relationship between engine components as well as the overall engine performance at different operating modes,on the basis of a newly developed nonlinear component-based ACE performance model.Results show that at all four working modes,turning down the high pressure compressor variable stator vane,the low pressure turbine variable nozzle,the nozzle throat area,and turning up the core-driven fan stage variable stator vane,the high pressure turbine variable nozzle can increase the thrust at the expense of a higher high pressure turbine inlet total temperature.However,the influences of these adjustments on the trends of various engine components＇ working points and working lines as well as the ratio of the rotation speed difference are different from each other.The above results provide valuable guidance and advice for engine combined control schedule design.展开更多
The precise control of turbofan engines thrust is an important guarantee for an aircraft to obtain good flight performance and a challenge due to complex nonlinear dynamics of engines and time-varying parameters. The ...The precise control of turbofan engines thrust is an important guarantee for an aircraft to obtain good flight performance and a challenge due to complex nonlinear dynamics of engines and time-varying parameters. The main difficulties lie in the following two aspects. Firstly, it is hard to obtain an accurate kinetic model for the turbofan engine. Secondly, some model parameters often change in different flight conditions and states and even fluctuate sharply in some cases. These variable parameters bring huge challenge for the turbofan engine control. To solve the turbofan engine control problem, this paper presents a non-affine parameter-dependent Linear Parameter Varying(LPV) model-based adaptive control approach. In this approach, polynomial-based LPV modeling method is firstly employed to obtain the basis matrices, and then the Radial Basis Function Neural Networks(RBFNN) is introduced for the online estimation of the non-affine model parameters to improve the simulation performance. LPV model-based Linear Matrix Inequality(LMI) control method is applied to derive the control law. A robust control term is introduced to fix the estimation error of the nonlinear time-varying model parameters for better control performance. Finally, the Lyapunov stability analysis is performed to ensure the asymptotical convergence of the closed loop system. The simulation results show that the states of the engine can change smoothly and the thrust of the engine can accurately follow the desired trajectory, indicating that the proposed control approach is effective. The contribution of this work lies in the combination of linear system control and nonlinear system control methods to design an effective controller for the turbofan engine and to provide a new way for turbofan engine control research.展开更多
In practice, some sensors of aircraft engines naturally fail to obtain an acceptable measurement for control propose, which will severely degrade the system performance and even deactivate the limit protection functio...In practice, some sensors of aircraft engines naturally fail to obtain an acceptable measurement for control propose, which will severely degrade the system performance and even deactivate the limit protection function. This paper proposes an adaptive strategy for the limit protection task under unreliable measurement. With the help of a nominal system, an online estimator with gradient adaption law and low-pass filter is devised to evaluate output uncertainty.Based on the estimation result, a sliding mode controller is designed by defining a sliding surface and deriving a control law. Using Lyapunov theorem, the stability of the online estimator and the closed-loop system is detailedly proven. Simulations based on a reliable turbofan model are presented, which verify the stability and effectiveness of the proposed method. Simulation results show that the online estimator can operate against the measurement noise, and the sliding controller can keep relevant outputs within their limits despite slow-response sensors.展开更多
In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support ve...In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support vector regression （MRR-LSSVR） machine is proposed. Firstly, the PS algorithm is designed to choose the most reasonable inputs of the adaptive module. During this process, a wrapper criterion based on least square support vector regression （LSSVR） machine is adopted, which can not only reduce computational complexity but also enhance generalization performance. Secondly, with the input variables determined by the PS algorithm, a mapping model of engine parameter estimation is trained off-line using MRR-LSSVR, which has a satisfying accuracy within 5＆. Finally, based on a numerical simulation platform of an integrated helicopter/ turbo-shaft engine system, an adaptive turbo-shaft engine model is developed and tested in a certain flight envelope. Under the condition of single or multiple engine components being degraded, many simulation experiments are carried out, and the simulation results show the effectiveness and validity of the proposed adaptive modeling method.展开更多
A decentralized model reference adaptive control (MRAC) scheme is proposed and applied to design a multivariable control system of a dual-spool turbofan engine.Simulation studies show good static and dynamic performan...A decentralized model reference adaptive control (MRAC) scheme is proposed and applied to design a multivariable control system of a dual-spool turbofan engine.Simulation studies show good static and dynamic performance of the system over the fullflight envelope. Simulation results also show the good effectiveness of reducing interactionin the multivariable system with significant coupling. The control system developed has awide frequency band to satisfy the strict engineering requirement and is practical for engineering applications.展开更多
A fault tolerant control methodology based adaptive sliding mode(ASM) backstepping is proposed for near space vehicle(NSV) attitude control system under engine faults. The proposed scheme combined adaptive backsteppin...A fault tolerant control methodology based adaptive sliding mode(ASM) backstepping is proposed for near space vehicle(NSV) attitude control system under engine faults. The proposed scheme combined adaptive backstepping with the sliding mode control strategy could guarantee the system’s stability and track desired signals under external disturbances and engine faults. Firstly, attitude mode description and the engine faulty model are given. Secondly, a nominal control law is designed.Thirdly, a sliding mode observer is given later in order to estimate both the information of engine faults and external disturbances. An adaptive sliding mode technology based on the previous nominal control law is developed via updating faulty parameters. Finally,analyze the system’s fault-tolerant performance and reliability through experiment simulation, which verifies the proposed design of fault-tolerant control can tolerate engine faults, as well as the strong robustness for external disturbance.展开更多
Front Variable Area Bypass Injector(Front-VABI) is a component of the Adaptive Cycle Engine(ACE) with important variable-cycle features. The performance of Front-VABI has a direct impact on the performance and stabili...Front Variable Area Bypass Injector(Front-VABI) is a component of the Adaptive Cycle Engine(ACE) with important variable-cycle features. The performance of Front-VABI has a direct impact on the performance and stability of ACE, but the current ACE performance model uses approximate models for Front-VABI performance calculation. In this work, a multi-fidelity simulation based on a de-coupled method is developed which delivers a more accurate calculation of the Front-VABI performance based on Computational Fluid Dynamics(CFD) simulation. This simulation method proposes a form of Front-VABI characteristic and its matching calculation method between it and the ACE performance model, constructs a coupling method between the(2-D) Front-VABI model and the(0-D) ACE performance model. The result shows, when ACE works in triple bypass mode, the approximate model cannot account for the effect of FrontVABI pressure loss on Core Driven Fan Stage(CDFS) design pressure ratio, and the calculated error of high-pressure turbine inlet total temperature is more than 40 K in mode transition condition(the transition operating condition between triple bypass mode and double bypass mode). In double bypass mode, the approximate model can better simulate the performance of FrontVABI by considering the local loss of area expansion. This method can be applied to the performance-optimized design of Front-VABI and the ACE control law design during mode transition.展开更多
In this paper, the control problem of auxiliary power unit （APU） for hybrid electric vehicles is investigated. An adaptive controller is provided to achieve the coordinated control between the engine speed and the b...In this paper, the control problem of auxiliary power unit （APU） for hybrid electric vehicles is investigated. An adaptive controller is provided to achieve the coordinated control between the engine speed and the battery charging voltage. The proposed adaptive coordinated control laws for the throttle angle of the engine and the voltage of the power-converter can guarantee not only the asymptotic tracking performance of the engine speed and the regulation of the battery charging voltage, but also the robust stability of the closed loop system under external load changes. Simulation results are given to verify the performance of the proposed adaptive controller.展开更多
The alternative working modes and flexible working states are the outstanding features of an adaptive cycle engine, with a proper control schedule design being the only way to exploit the performance of such an engine...The alternative working modes and flexible working states are the outstanding features of an adaptive cycle engine, with a proper control schedule design being the only way to exploit the performance of such an engine. However, unreasonable design in the control schedule causes not only performance deterioration but also serious aerodynamic stability problems. Thus, in this work,a hybrid optimization method that automatically chooses the working modes and identifies the optimal and smooth control schedules is proposed, by combining the differential evolution algorithm and the Latin hypercube sampling method. The control schedule architecture does not only optimize the engine steady-state performance under different working modes but also solves the control-schedule discontinuity problem, especially during mode transition. The optimal control schedules are continuous and almost monotonic, and hence are strongly suitable for a control system, and are designed for two different working conditions, i.e., supersonic and subsonic throttling,which proves that the proposed hybrid method applies to various working conditions. The evaluation demonstrates that the proposed control method optimizes the engine performance, the surge margin of the compression components, and the range of the thrust during throttling.展开更多
While stromal interactions are essential in cancer adaptation to hormonal therapies,the effects of bone stroma and androgen deprivation on cancer progression in bone are poorly understood.Here,we tissue-engineered and...While stromal interactions are essential in cancer adaptation to hormonal therapies,the effects of bone stroma and androgen deprivation on cancer progression in bone are poorly understood.Here,we tissue-engineered and validated an in vitro microtissue model of osteoblastic bone metastases,and used it to study the effects of androgen deprivation in this microenvironment.The model was established by culturing primary human osteoprogenitor cells on melt electrowritten polymer scaffolds,leading to a mineralized osteoblast-derived microtissue containing,in a 3D setting,viable osteoblastic cells,osteocytic cells,and appropriate expression of osteoblast/osteocyte-derived mRNA and proteins,and mineral content.Direct co-culture of androgen receptordependent/ independent cell lines (LNCaP,C4-2B,and PC3) led cancer cells to display functional and molecular features as observed in vivo.Co-cultured cancer cells showed increased affinity to the microtissues,as a function of their bone metastatic potential.Cocultures led to alkaline phosphatase and collagen-I upregulation and sclerostin downregulation,consistent with the clinical marker profile of osteoblastic bone metastases.LNCaP showed a significant adaptive response under androgen deprivation in the microtissues,with the notable appearance of neuroendocrine transdifferentiation features and increased expression of related markers (dopa decarboxylase,enolase 2).Androgen deprivation affected the biology of the metastatic microenvironment with stronger upregulation of androgen receptor,alkaline phosphatase,and dopa decarboxylase,as seen in the transition towards resistance.The unique microtissues engineered here represent a substantial asset to determine the involvement of the human bone microenvironment in prostate cancer progression and response to a therapeutic context in this microenvironment.展开更多
A model reference adaptive control(MRAC)with smooth switching scheme was proposed for piecewise linear systems,and the method was utilized in turbofan engine control to avoid the discontinuity of control input.In this...A model reference adaptive control(MRAC)with smooth switching scheme was proposed for piecewise linear systems,and the method was utilized in turbofan engine control to avoid the discontinuity of control input.In this scheme,each sub-region of the operating envelope had its own MRAC controller,and smooth indicator function based smooth switching scheme was introduced to switch multiple controllers smoothly at the boundary of adjacent sub-regions.The Lyapunov stability analysis indicated that the proposed smooth switching scheme can guarantee the convergence of the closed-loop system during the controllers switching.The tracking error system was converted into a switched system to analyze the global stability of the closed-loop system.The advantage of the method was that the chattering of system output and instability caused by asynchronous switching can be eliminated.The simulation illustrates the effectiveness of the proposed control scheme in comparison with the existing MRAC controller with gain scheduling for turbofan engine.展开更多
A new on-line fault detection and isolation （FDI） scheme proposed for engines using an adaptive neural network classifier is evaluated for a wide range of operational modes to check the robustness of the scheme in t...A new on-line fault detection and isolation （FDI） scheme proposed for engines using an adaptive neural network classifier is evaluated for a wide range of operational modes to check the robustness of the scheme in this paper. The neural classifier is adaptive to cope with the significant parameter uncertainty, disturbances, and environment changes. The developed scheme is capable of diagnosing faults in on-line mode and the FDI for the closed-loop system with can be directly implemented in an on-board crankshaft speed feedback is investigated by diagnosis system （hardware）. The robustness of testing it for a wide range of operational modes including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all these changes occurring at the same time. The evaluations are performed using a mean value engine model （MVEM）, which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.展开更多
The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search sta...The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.展开更多
Immunotherapy offers the promise of a potential cure for cancer,yet achieving the desired therapeutic effect can be challenging due to the immunosuppressive tumor microenvironments(TMEs) present in some tumors.Therefo...Immunotherapy offers the promise of a potential cure for cancer,yet achieving the desired therapeutic effect can be challenging due to the immunosuppressive tumor microenvironments(TMEs) present in some tumors.Therefore,robust immune system activation is crucial to enhance the efficacy of cancer immunotherapy in clinical applications.Bacteria have shown the ability to target the hypoxic TMEs while activating both innate and adaptive immune responses.Engineered bacteria,modified through chemical or biological methods,can be endowed with specific physiological properties,such as diverse surface antigens,metabolites,and improved biocompatibility.These unique characteristics give engineered bacteria distinct advantages in stimulating anti-cancer immune responses.This review explores the potential regulatory mechanisms of engineered bacteria in modulating both innate and adaptive immunity while also forecasting the future development and challenges of using engineered bacteria in clinical cancer immunotherapy.展开更多
The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by sub...The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by substantial time and economic costs.To address these challenges,in this work,we report ChemELLM,a domain‐specific large language model(LLM)with 70 billion parameters for chemical engineering.ChemELLM demonstrates state‐of‐the‐art performance across critical tasks ranging from foundational understanding to professional problem‐solving.It outperforms mainstream LLMs(e.g.,O1‐Preview,GPT‐4o,and DeepSeek‐R1)on ChemEBench,the first multidimensional benchmark for chemical engineering,which encompasses 15 dimensions across 101 distinct essential tasks.To support robust model development,we curated ChemEData,a purpose‐built dataset containing 19 billion tokens for pre‐training and 1 billion tokens for fine‐tuning.This work establishes a new paradigm for artificial intelligence‐driven innovation,bridging the gap between laboratory‐scale innovation and industrial‐scale implementation,thus accelerating technological advancement in chemical engineering.ChemELLM is publicly available at https://chemindustry.iflytek.com/chat.展开更多
Intelligent Adaptive Control(AC) has remarkable advantages in the control system design of aero-engine which has strong nonlinearity and uncertainty. Inspired by the Nonlinear Autoregressive Moving Average(NARMA)-L2 a...Intelligent Adaptive Control(AC) has remarkable advantages in the control system design of aero-engine which has strong nonlinearity and uncertainty. Inspired by the Nonlinear Autoregressive Moving Average(NARMA)-L2 adaptive control, a novel Nonlinear State Space Equation(NSSE) based Adaptive neural network Control(NSSE-AC) method is proposed for the turbo-shaft engine control system design. The proposed NSSE model is derived from a special neural network with an extra layer, and the rotor speed of the gas turbine is taken as the main state variable which makes the NSSE model be able to capture the system dynamic better than the NARMA-L2 model. A hybrid Recursive Least-Square and Levenberg-Marquardt(RLS-LM) algorithm is advanced to perform the online learning of the neural network, which further enhances both the accuracy of the NSSE model and the performance of the adaptive controller. The feedback correction is also utilized in the NSSE-AC system to eliminate the steady-state tracking error. Simulation results show that, compared with the NARMA-L2 model, the NSSE model of the turboshaft engine is more accurate. The maximum modeling error is decreased from 5.92% to 0.97%when the LM algorithm is introduced to optimize the neural network parameters. The NSSE-AC method can not only achieve a better main control loop performance than the traditional controller but also limit all the constraint parameters efficiently with quick and accurate switching responses even if component degradation exists. Thus, the effectiveness of the NSSE-AC method is validated.展开更多
基金supported by National Natural Science Foundation of China(No.52302472)。
文摘The development of the adaptive cycle engine is a crucial direction of advanced fighter power sources in the near future.However,this new technology brings more uncertainty to the design of the control system.To address the versatile thrust demand under complex dynamic characteristics of the adaptive cycle engine,this paper proposes a direct thrust estimation and control method based on the Model-Free Adaptive Control(MFAC)algorithm.First,an improved Sliding Mode Control-MFAC(SMC-MFAC)algorithm has been developed by introducing a sliding mode variable structure into the standard Full Format Dynamic Linearization-MFAC(FFDL-MFAC)and designing self-adaptive weight coefficients.Then a trivariate double-loop direct thrust control structure with a controller-based thrust estimator and an outer command compensation loop has been established.Through thrust feedback and command correction,accurate control under multi-mode and operation conditions is achieved.The main contribution of this paper is the improved algorithm that combines the tracking capability of the MFAC and the robustness of the SMC,thus enhancing the dynamic performance.Considering the requirements of the online thrust feedback,the designed MFAC-based thrust estimator significantly speeds up the calculation.Additionally,the proposed command correction module can achieve the adaptive thrust control without affecting the operation of the inner loop.Simulations and Hardware-in-Loop(HIL)experiments have been performed on an adaptive cycle engine component-level model to investigate the estimation and control effect under different modes and health conditions.The results demonstrate that both the thrust estimation precision and operation speed are significantly improved compared with Extended Kalman Filter(EKF).Furthermore,the system can accelerate the response of the controlled plant,reduce the overshoot,and realize the thrust recovery within the safety range when the engine encounters the degradation.
基金supported in part by the National Natural Science Foundation of China(No.52372389)the Jiangsu Province Excellent Postdoctoral Program of China(No.2023ZB494)+1 种基金the Basic Research Program of Jiangsu Province,China(No.BK20241412)the National Science Foundation for Post-doctoral Scientists of China(No.2024M754131)。
文摘The acceleration and mode transition performance are two significant performances of Adaptive Cycle Engine(ACE).However,separating the processes of acceleration and mode transition will slow down the response speed of thrust.Therefore,this paper proposes a multi-mode acceleration optimization control method that simultaneously performs ACE acceleration and mode transition.Firstly,an ACE component model with inlet flow characteristics was established,and the performance before and after mode transition were analyzed.Secondly,the principle of ACE acceleration optimization was analyzed,and the Front Variable Area Bypass Injector(FVABI)and Mode Selection Valve(MSV)were adopted in the acceleration process.Finally,based on the Sequential Quadratic Programming(SQP)algorithm,considering the degradation effects of engine components,we optimize the acceleration control plan for fuel and variable geometry mechanisms.The simulation results show that at the subsonic cruise point,the ACE multi-mode acceleration optimization control method can shorten the acceleration time from idle to middle state by 30.33%,and accelerate the thrust response speed by 33.72%.When the compressor flow rate of ACE deteriorates by 2% and the high-pressure turbine efficiency deteriorates by 4%,the adaptive acceleration control plan increases the high-pressure speed by 2.13% and thrust by about 6.82%;within the flight envelope,the acceleration time is reduced by more than 25%,and the thrust response speed is increased by more than 20%.
文摘The design of a turbofan rotor speed control system, using model reference adaptive control（MRAC） method with input and output measurements, is discussed for the purpose of practical application. The nonlinear compensator based on functional link neural network is used to deal with the engine nonlinearity and the hardware-in-loop simulation is also developed. The results show that the nonlinear MRAC controller has the adequate performance of compensating and adapting nonlinearity arising from the change of engine state or working environment. Such feature demonstrates potential practical applications of MRAC for aeroengine control system.
基金supported by the National Natural Science Foundation of China(No.51206005)Collaborative Innovation Center of Advanced Aero-Engine of China
文摘As a novel aero-engine concept,adaptive cycle aero-engines（ACEs） are attracting wide attention in the international aviation industry due to their potential superior task adaptability along a wide flight regime.However,this superior task adaptability can only be demonstrated through proper combined engine control schedule design.It has resulted in an urgent need to investigate the effect of each variable geometry modulation on engine performance and stability.Thus,the aim of this paper is to predict and discuss the effect of each variable geometry modulation on the matching relationship between engine components as well as the overall engine performance at different operating modes,on the basis of a newly developed nonlinear component-based ACE performance model.Results show that at all four working modes,turning down the high pressure compressor variable stator vane,the low pressure turbine variable nozzle,the nozzle throat area,and turning up the core-driven fan stage variable stator vane,the high pressure turbine variable nozzle can increase the thrust at the expense of a higher high pressure turbine inlet total temperature.However,the influences of these adjustments on the trends of various engine components＇ working points and working lines as well as the ratio of the rotation speed difference are different from each other.The above results provide valuable guidance and advice for engine combined control schedule design.
基金supported by the National Natural Science Foundation of China(No.51766011)the Aeronautical Science Foundation of China(No.2014ZB56002)
文摘The precise control of turbofan engines thrust is an important guarantee for an aircraft to obtain good flight performance and a challenge due to complex nonlinear dynamics of engines and time-varying parameters. The main difficulties lie in the following two aspects. Firstly, it is hard to obtain an accurate kinetic model for the turbofan engine. Secondly, some model parameters often change in different flight conditions and states and even fluctuate sharply in some cases. These variable parameters bring huge challenge for the turbofan engine control. To solve the turbofan engine control problem, this paper presents a non-affine parameter-dependent Linear Parameter Varying(LPV) model-based adaptive control approach. In this approach, polynomial-based LPV modeling method is firstly employed to obtain the basis matrices, and then the Radial Basis Function Neural Networks(RBFNN) is introduced for the online estimation of the non-affine model parameters to improve the simulation performance. LPV model-based Linear Matrix Inequality(LMI) control method is applied to derive the control law. A robust control term is introduced to fix the estimation error of the nonlinear time-varying model parameters for better control performance. Finally, the Lyapunov stability analysis is performed to ensure the asymptotical convergence of the closed loop system. The simulation results show that the states of the engine can change smoothly and the thrust of the engine can accurately follow the desired trajectory, indicating that the proposed control approach is effective. The contribution of this work lies in the combination of linear system control and nonlinear system control methods to design an effective controller for the turbofan engine and to provide a new way for turbofan engine control research.
文摘In practice, some sensors of aircraft engines naturally fail to obtain an acceptable measurement for control propose, which will severely degrade the system performance and even deactivate the limit protection function. This paper proposes an adaptive strategy for the limit protection task under unreliable measurement. With the help of a nominal system, an online estimator with gradient adaption law and low-pass filter is devised to evaluate output uncertainty.Based on the estimation result, a sliding mode controller is designed by defining a sliding surface and deriving a control law. Using Lyapunov theorem, the stability of the online estimator and the closed-loop system is detailedly proven. Simulations based on a reliable turbofan model are presented, which verify the stability and effectiveness of the proposed method. Simulation results show that the online estimator can operate against the measurement noise, and the sliding controller can keep relevant outputs within their limits despite slow-response sensors.
基金co-supported by Aeronautical Science Foundation of China (No. 2010ZB52011)Funding of Jiangsu Innovation Program for Graduate Education (No.CXLX11_0213)
文摘In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support vector regression （MRR-LSSVR） machine is proposed. Firstly, the PS algorithm is designed to choose the most reasonable inputs of the adaptive module. During this process, a wrapper criterion based on least square support vector regression （LSSVR） machine is adopted, which can not only reduce computational complexity but also enhance generalization performance. Secondly, with the input variables determined by the PS algorithm, a mapping model of engine parameter estimation is trained off-line using MRR-LSSVR, which has a satisfying accuracy within 5＆. Finally, based on a numerical simulation platform of an integrated helicopter/ turbo-shaft engine system, an adaptive turbo-shaft engine model is developed and tested in a certain flight envelope. Under the condition of single or multiple engine components being degraded, many simulation experiments are carried out, and the simulation results show the effectiveness and validity of the proposed adaptive modeling method.
文摘A decentralized model reference adaptive control (MRAC) scheme is proposed and applied to design a multivariable control system of a dual-spool turbofan engine.Simulation studies show good static and dynamic performance of the system over the fullflight envelope. Simulation results also show the good effectiveness of reducing interactionin the multivariable system with significant coupling. The control system developed has awide frequency band to satisfy the strict engineering requirement and is practical for engineering applications.
基金supported by the National Natural Science Foundation of China(6140321061601228+3 种基金61603191)the Natural Science Foundation of Jiangsu(BK20161021)the Nanjing University of Posts and Telecommunications Science Foundation(NY214173)the Open Program of Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing(3DL201607)
文摘A fault tolerant control methodology based adaptive sliding mode(ASM) backstepping is proposed for near space vehicle(NSV) attitude control system under engine faults. The proposed scheme combined adaptive backstepping with the sliding mode control strategy could guarantee the system’s stability and track desired signals under external disturbances and engine faults. Firstly, attitude mode description and the engine faulty model are given. Secondly, a nominal control law is designed.Thirdly, a sliding mode observer is given later in order to estimate both the information of engine faults and external disturbances. An adaptive sliding mode technology based on the previous nominal control law is developed via updating faulty parameters. Finally,analyze the system’s fault-tolerant performance and reliability through experiment simulation, which verifies the proposed design of fault-tolerant control can tolerate engine faults, as well as the strong robustness for external disturbance.
基金funded by National Natural Science Foundation of China(Nos.51776010 and 91860205)National Science and Technology Major Project,China(No.2017-I0001-0001)。
文摘Front Variable Area Bypass Injector(Front-VABI) is a component of the Adaptive Cycle Engine(ACE) with important variable-cycle features. The performance of Front-VABI has a direct impact on the performance and stability of ACE, but the current ACE performance model uses approximate models for Front-VABI performance calculation. In this work, a multi-fidelity simulation based on a de-coupled method is developed which delivers a more accurate calculation of the Front-VABI performance based on Computational Fluid Dynamics(CFD) simulation. This simulation method proposes a form of Front-VABI characteristic and its matching calculation method between it and the ACE performance model, constructs a coupling method between the(2-D) Front-VABI model and the(0-D) ACE performance model. The result shows, when ACE works in triple bypass mode, the approximate model cannot account for the effect of FrontVABI pressure loss on Core Driven Fan Stage(CDFS) design pressure ratio, and the calculated error of high-pressure turbine inlet total temperature is more than 40 K in mode transition condition(the transition operating condition between triple bypass mode and double bypass mode). In double bypass mode, the approximate model can better simulate the performance of FrontVABI by considering the local loss of area expansion. This method can be applied to the performance-optimized design of Front-VABI and the ACE control law design during mode transition.
文摘In this paper, the control problem of auxiliary power unit （APU） for hybrid electric vehicles is investigated. An adaptive controller is provided to achieve the coordinated control between the engine speed and the battery charging voltage. The proposed adaptive coordinated control laws for the throttle angle of the engine and the voltage of the power-converter can guarantee not only the asymptotic tracking performance of the engine speed and the regulation of the battery charging voltage, but also the robust stability of the closed loop system under external load changes. Simulation results are given to verify the performance of the proposed adaptive controller.
基金funded by National Nature Science Foundation of China(Nos.51776010 and 91860205)supported by the Academic Excellence Foundation of BUAA for PhD Students,China。
文摘The alternative working modes and flexible working states are the outstanding features of an adaptive cycle engine, with a proper control schedule design being the only way to exploit the performance of such an engine. However, unreasonable design in the control schedule causes not only performance deterioration but also serious aerodynamic stability problems. Thus, in this work,a hybrid optimization method that automatically chooses the working modes and identifies the optimal and smooth control schedules is proposed, by combining the differential evolution algorithm and the Latin hypercube sampling method. The control schedule architecture does not only optimize the engine steady-state performance under different working modes but also solves the control-schedule discontinuity problem, especially during mode transition. The optimal control schedules are continuous and almost monotonic, and hence are strongly suitable for a control system, and are designed for two different working conditions, i.e., supersonic and subsonic throttling,which proves that the proposed hybrid method applies to various working conditions. The evaluation demonstrates that the proposed control method optimizes the engine performance, the surge margin of the compression components, and the range of the thrust during throttling.
基金N.B.:IHBI ECR grant,Advance Queensland(AQ)Maternity Fund Award from the Queensland Government(DSITI),Young Researcher Award(2017-YR-RoW-9)from Lush(UK)supporting non-animal testing alternatives,National Health and Medical Research Council(NHMRC)Peter Doherty Early Career Research Fellowship(RF)(APP1091734)+5 种基金John Mills Young Investigator Award(YI0715)from the Prostate Cancer Foundation of Australia(PCFA)P.A.T.:Vice Chancellor’s RF(QUT)and AQ RF(QLD)J.A.C.:NHMRC PRFD.W.H.:Humboldt RF,ARC Industrial Transformation Training Center in Additive Biomanufacturing(IC160100026)NHMRC,World Cancer Foundation,National Breast Cancer Foundation,PCFA.D.W.H.,J.A.C.,C.C.N.:Movember Revolutionary Team Award(from Movember and PCFA).APCRC-Qthe Translational Research Institute are supported by grants from the Australian Government
文摘While stromal interactions are essential in cancer adaptation to hormonal therapies,the effects of bone stroma and androgen deprivation on cancer progression in bone are poorly understood.Here,we tissue-engineered and validated an in vitro microtissue model of osteoblastic bone metastases,and used it to study the effects of androgen deprivation in this microenvironment.The model was established by culturing primary human osteoprogenitor cells on melt electrowritten polymer scaffolds,leading to a mineralized osteoblast-derived microtissue containing,in a 3D setting,viable osteoblastic cells,osteocytic cells,and appropriate expression of osteoblast/osteocyte-derived mRNA and proteins,and mineral content.Direct co-culture of androgen receptordependent/ independent cell lines (LNCaP,C4-2B,and PC3) led cancer cells to display functional and molecular features as observed in vivo.Co-cultured cancer cells showed increased affinity to the microtissues,as a function of their bone metastatic potential.Cocultures led to alkaline phosphatase and collagen-I upregulation and sclerostin downregulation,consistent with the clinical marker profile of osteoblastic bone metastases.LNCaP showed a significant adaptive response under androgen deprivation in the microtissues,with the notable appearance of neuroendocrine transdifferentiation features and increased expression of related markers (dopa decarboxylase,enolase 2).Androgen deprivation affected the biology of the metastatic microenvironment with stronger upregulation of androgen receptor,alkaline phosphatase,and dopa decarboxylase,as seen in the transition towards resistance.The unique microtissues engineered here represent a substantial asset to determine the involvement of the human bone microenvironment in prostate cancer progression and response to a therapeutic context in this microenvironment.
文摘A model reference adaptive control(MRAC)with smooth switching scheme was proposed for piecewise linear systems,and the method was utilized in turbofan engine control to avoid the discontinuity of control input.In this scheme,each sub-region of the operating envelope had its own MRAC controller,and smooth indicator function based smooth switching scheme was introduced to switch multiple controllers smoothly at the boundary of adjacent sub-regions.The Lyapunov stability analysis indicated that the proposed smooth switching scheme can guarantee the convergence of the closed-loop system during the controllers switching.The tracking error system was converted into a switched system to analyze the global stability of the closed-loop system.The advantage of the method was that the chattering of system output and instability caused by asynchronous switching can be eliminated.The simulation illustrates the effectiveness of the proposed control scheme in comparison with the existing MRAC controller with gain scheduling for turbofan engine.
基金This work was supported by Universities UK,Faculty of Technology and Environment and School of Engineering,Liverpool John Moores University,UK.
文摘A new on-line fault detection and isolation （FDI） scheme proposed for engines using an adaptive neural network classifier is evaluated for a wide range of operational modes to check the robustness of the scheme in this paper. The neural classifier is adaptive to cope with the significant parameter uncertainty, disturbances, and environment changes. The developed scheme is capable of diagnosing faults in on-line mode and the FDI for the closed-loop system with can be directly implemented in an on-board crankshaft speed feedback is investigated by diagnosis system （hardware）. The robustness of testing it for a wide range of operational modes including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all these changes occurring at the same time. The evaluations are performed using a mean value engine model （MVEM）, which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.
基金Project of Key Science and Technology of the Henan Province(No.202102310259)Henan Province University Scientific and Technological Innovation Team(No.18IRTSTHN009).
文摘The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.
基金supported by the Science and Technology Research Project of Jilin Education Bureau(No.JJKH20230804KJ)。
文摘Immunotherapy offers the promise of a potential cure for cancer,yet achieving the desired therapeutic effect can be challenging due to the immunosuppressive tumor microenvironments(TMEs) present in some tumors.Therefore,robust immune system activation is crucial to enhance the efficacy of cancer immunotherapy in clinical applications.Bacteria have shown the ability to target the hypoxic TMEs while activating both innate and adaptive immune responses.Engineered bacteria,modified through chemical or biological methods,can be endowed with specific physiological properties,such as diverse surface antigens,metabolites,and improved biocompatibility.These unique characteristics give engineered bacteria distinct advantages in stimulating anti-cancer immune responses.This review explores the potential regulatory mechanisms of engineered bacteria in modulating both innate and adaptive immunity while also forecasting the future development and challenges of using engineered bacteria in clinical cancer immunotherapy.
文摘The development of chemical technologies,which involves a multistage process covering laboratory research,scale‐up to industrial deployment,and necessitates interdisciplinary collaboration,is often accompanied by substantial time and economic costs.To address these challenges,in this work,we report ChemELLM,a domain‐specific large language model(LLM)with 70 billion parameters for chemical engineering.ChemELLM demonstrates state‐of‐the‐art performance across critical tasks ranging from foundational understanding to professional problem‐solving.It outperforms mainstream LLMs(e.g.,O1‐Preview,GPT‐4o,and DeepSeek‐R1)on ChemEBench,the first multidimensional benchmark for chemical engineering,which encompasses 15 dimensions across 101 distinct essential tasks.To support robust model development,we curated ChemEData,a purpose‐built dataset containing 19 billion tokens for pre‐training and 1 billion tokens for fine‐tuning.This work establishes a new paradigm for artificial intelligence‐driven innovation,bridging the gap between laboratory‐scale innovation and industrial‐scale implementation,thus accelerating technological advancement in chemical engineering.ChemELLM is publicly available at https://chemindustry.iflytek.com/chat.
基金co-supported by the National Science and Technology Major Project, China (No. J2019-Ⅰ-0010-0010)the Project funded by China Postdoctoral Science Foundation (No. 2021M701692)+3 种基金the Fundamental Research Funds for the Central Universities, China (No. NS2022029)the Postgraduate Research & Practice Innovation Program of NUAA, China (No. xcxjh20220206)the National Natural Science Foundation of China (No. 51976089)Jiangsu Funding Program for Excellent Postdoctoral Talent, China (No. 2022ZB202)。
文摘Intelligent Adaptive Control(AC) has remarkable advantages in the control system design of aero-engine which has strong nonlinearity and uncertainty. Inspired by the Nonlinear Autoregressive Moving Average(NARMA)-L2 adaptive control, a novel Nonlinear State Space Equation(NSSE) based Adaptive neural network Control(NSSE-AC) method is proposed for the turbo-shaft engine control system design. The proposed NSSE model is derived from a special neural network with an extra layer, and the rotor speed of the gas turbine is taken as the main state variable which makes the NSSE model be able to capture the system dynamic better than the NARMA-L2 model. A hybrid Recursive Least-Square and Levenberg-Marquardt(RLS-LM) algorithm is advanced to perform the online learning of the neural network, which further enhances both the accuracy of the NSSE model and the performance of the adaptive controller. The feedback correction is also utilized in the NSSE-AC system to eliminate the steady-state tracking error. Simulation results show that, compared with the NARMA-L2 model, the NSSE model of the turboshaft engine is more accurate. The maximum modeling error is decreased from 5.92% to 0.97%when the LM algorithm is introduced to optimize the neural network parameters. The NSSE-AC method can not only achieve a better main control loop performance than the traditional controller but also limit all the constraint parameters efficiently with quick and accurate switching responses even if component degradation exists. Thus, the effectiveness of the NSSE-AC method is validated.
