A novel turbofan Direct Thrust Control(DTC)architecture based on Linear ParameterVarying(LPV)approach for a two-spool turbofan engine thrust control is proposed in this paper.Instead of transforming thrust command to ...A novel turbofan Direct Thrust Control(DTC)architecture based on Linear ParameterVarying(LPV)approach for a two-spool turbofan engine thrust control is proposed in this paper.Instead of transforming thrust command to shaft speed command and pressure ratio command,the thrust will be directly controlled by an optimal controller with two control variables.LPV model of the engine is established for the designing of thrust estimator and controller.A robust LPV H∞filter is introduced to estimate the unmeasurable thrust according to measurable engine states.The thrust estimation error system is proved to be Affinely Quadratically Stable(AQS)in the whole parameter box with a prescribed H∞performance indexγ.Due to the existence of overdetermined equations,the solving of controller parameters is a multi-solution problem.Therefore,Particle Swarm Optimization(PSO)algorithm is used to optimize the controller parameters to obtain satisfactory control performance based on the engine’s LPV model.Numerical simulations show that the thrust estimator can acquire smooth and accurate estimating results when sensor noise exists.The optimal controller can receive desired control performance both in steady and transition control tasks within the engine working states above the idle,verifying the effectiveness of the proposed DTC architecture’s application in thrust direct control problem.展开更多
A nonlinear model predictive control method based on fuzzy-Sequential Quadratic Programming(SQP)for direct thrust control is proposed in this paper for the sake of improving the accuracy of thrust control.The designed...A nonlinear model predictive control method based on fuzzy-Sequential Quadratic Programming(SQP)for direct thrust control is proposed in this paper for the sake of improving the accuracy of thrust control.The designed control system includes four parts,namely a predictive model,rolling optimization,online correction,and feedback correction.Considering the strong nonlinearity of engine,a predictive model is established by Back Propagation(BP)neural network for the entire flight envelope,whose input and output are determined with random forest algorithm and actual situation analysis.Rolling optimization typically uses SQP as the optimization algorithm,but SQP algorithm is easy to trap into local optimization.Therefore,the fuzzy-SQP algorithm is proposed to prevent this disadvantage using fuzzy algorithm to determine the initial value of SQP.In addition to the traditional three parts of model predictive control,an online correction module is added to improve the predictive accuracy of the predictive model in the predictive time domain.Simulation results show that the BP predictive model can reach a certain degree of predictive accuracy,and the proposed control system can achieve good tracking performance with the limited parameters within the safe range。展开更多
With the development of the aircraft gas turbine engine, a control system should be able to achieve effective thrust control to gain better operability. The main contribution of this paper is to develop a novel direct...With the development of the aircraft gas turbine engine, a control system should be able to achieve effective thrust control to gain better operability. The main contribution of this paper is to develop a novel direct thrust control approach based on an improved model predictive control method through a strategy that reduces the dimension of control sequence. It can not only achieve normal direct thrust control tasks but also maximize the thrust level within the safe operation boundaries. Only the action of switching the objective functions is required to achieve the switch of these two thrust control modes while there is no modification to the control structure. Besides,a shorter control sequence is defined for multivariable control by updating only one control variable at every simulation time instant. Therefore, the time requirement for the solving process of the optimal control sequence is reduced. The proposed controller is implemented to a twin-spool engine.Simulations are conducted in the wide flight envelope, and results show that the average timeconsumption can be reduced up to 65% in comparison with the standard model predictive control,and the thrust can be increased significantly when maximum thrust mode is implemented by using engine limit margins.展开更多
In order to realize direct thrust control instead of conventional sensors-based control for aero-engine, a thrust estimator with high accuracy is designed by using the boosting technique to improve the performance of ...In order to realize direct thrust control instead of conventional sensors-based control for aero-engine, a thrust estimator with high accuracy is designed by using the boosting technique to improve the performance of least squares support vector regression （LSSVR）. There exist two distinct features compared with the conven- tional boosting technique： （1） Sampling without replacement is used to avoid numerical instability for modeling LSSVR. （2） To realize the sparseness of LSSVR and reduce the computational complexity, only a subset of the training samples is used to construct LSSVR. Thus, this boosting method for LSSVR is called the boosting sparse LSSVR （BSLSSVR）. Finally, simulation results show that BSLSSVR-based thrust estimator can satisfy the requirement of direct thrust control, i.e. , maximum absolute value of relative error of thrust estimation is not more than 5‰.展开更多
In order to realize direct thrust control instead of traditional sensor-based control for aero-engines,it is indispensable to design a thrust estimator with high accuracy,so a scheme for thrust estimator design based ...In order to realize direct thrust control instead of traditional sensor-based control for aero-engines,it is indispensable to design a thrust estimator with high accuracy,so a scheme for thrust estimator design based on the least square support vector regression machine is proposed to solve this problem. Furthermore,numerical simulations confirm the effectiveness of our presented scheme. During the process of estimator design,a wrapper criterion that can not only reduce the computational complexity but also enhance the generalization performance is proposed to select variables as input variables for estimator.展开更多
基金supported by the National Science and Technology Major Project, China (No. 2017-V-0004-0054)
文摘A novel turbofan Direct Thrust Control(DTC)architecture based on Linear ParameterVarying(LPV)approach for a two-spool turbofan engine thrust control is proposed in this paper.Instead of transforming thrust command to shaft speed command and pressure ratio command,the thrust will be directly controlled by an optimal controller with two control variables.LPV model of the engine is established for the designing of thrust estimator and controller.A robust LPV H∞filter is introduced to estimate the unmeasurable thrust according to measurable engine states.The thrust estimation error system is proved to be Affinely Quadratically Stable(AQS)in the whole parameter box with a prescribed H∞performance indexγ.Due to the existence of overdetermined equations,the solving of controller parameters is a multi-solution problem.Therefore,Particle Swarm Optimization(PSO)algorithm is used to optimize the controller parameters to obtain satisfactory control performance based on the engine’s LPV model.Numerical simulations show that the thrust estimator can acquire smooth and accurate estimating results when sensor noise exists.The optimal controller can receive desired control performance both in steady and transition control tasks within the engine working states above the idle,verifying the effectiveness of the proposed DTC architecture’s application in thrust direct control problem.
基金supported by the Fundamental Research Enhancement Project,China(No.2017-JCJQ-ZD-047-21).
文摘A nonlinear model predictive control method based on fuzzy-Sequential Quadratic Programming(SQP)for direct thrust control is proposed in this paper for the sake of improving the accuracy of thrust control.The designed control system includes four parts,namely a predictive model,rolling optimization,online correction,and feedback correction.Considering the strong nonlinearity of engine,a predictive model is established by Back Propagation(BP)neural network for the entire flight envelope,whose input and output are determined with random forest algorithm and actual situation analysis.Rolling optimization typically uses SQP as the optimization algorithm,but SQP algorithm is easy to trap into local optimization.Therefore,the fuzzy-SQP algorithm is proposed to prevent this disadvantage using fuzzy algorithm to determine the initial value of SQP.In addition to the traditional three parts of model predictive control,an online correction module is added to improve the predictive accuracy of the predictive model in the predictive time domain.Simulation results show that the BP predictive model can reach a certain degree of predictive accuracy,and the proposed control system can achieve good tracking performance with the limited parameters within the safe range。
基金supported by China Scholarship Council(No.201906830081)。
文摘With the development of the aircraft gas turbine engine, a control system should be able to achieve effective thrust control to gain better operability. The main contribution of this paper is to develop a novel direct thrust control approach based on an improved model predictive control method through a strategy that reduces the dimension of control sequence. It can not only achieve normal direct thrust control tasks but also maximize the thrust level within the safe operation boundaries. Only the action of switching the objective functions is required to achieve the switch of these two thrust control modes while there is no modification to the control structure. Besides,a shorter control sequence is defined for multivariable control by updating only one control variable at every simulation time instant. Therefore, the time requirement for the solving process of the optimal control sequence is reduced. The proposed controller is implemented to a twin-spool engine.Simulations are conducted in the wide flight envelope, and results show that the average timeconsumption can be reduced up to 65% in comparison with the standard model predictive control,and the thrust can be increased significantly when maximum thrust mode is implemented by using engine limit margins.
基金Supported by the National Natural Science Foundation of China(50576033)the Aeronautical Science Foundation of China(04C52019)~~
文摘In order to realize direct thrust control instead of conventional sensors-based control for aero-engine, a thrust estimator with high accuracy is designed by using the boosting technique to improve the performance of least squares support vector regression （LSSVR）. There exist two distinct features compared with the conven- tional boosting technique： （1） Sampling without replacement is used to avoid numerical instability for modeling LSSVR. （2） To realize the sparseness of LSSVR and reduce the computational complexity, only a subset of the training samples is used to construct LSSVR. Thus, this boosting method for LSSVR is called the boosting sparse LSSVR （BSLSSVR）. Finally, simulation results show that BSLSSVR-based thrust estimator can satisfy the requirement of direct thrust control, i.e. , maximum absolute value of relative error of thrust estimation is not more than 5‰.
基金Sponsored by the National Natural Science Foundation of China ( Grant No 50576033)
文摘In order to realize direct thrust control instead of traditional sensor-based control for aero-engines,it is indispensable to design a thrust estimator with high accuracy,so a scheme for thrust estimator design based on the least square support vector regression machine is proposed to solve this problem. Furthermore,numerical simulations confirm the effectiveness of our presented scheme. During the process of estimator design,a wrapper criterion that can not only reduce the computational complexity but also enhance the generalization performance is proposed to select variables as input variables for estimator.
