A global optimization control for turbo-fan engine acceleration schedule design

Proceedings of the Institution of Mechanical Engineers, Part I:

Wang

Jin

et al. 2021

Self Cite

The modern advanced aero-engine control methods are onboard dynamic model–based algorithms. In this article, a novel aero-engine dynamic modeling method based on improved compact propulsion system dynamic model is proposed. The aero-engine model is divided into inlet, core engine, surge margin and nozzle models for establishing sub-model in the compact propulsion system dynamic model. The model of core engine is state variable model. The models of inlet, surge margin and nozzle are nonlinear models which are similar to the component level model. A new scheduling scheme for basepoint control vector, basepoint state vector and basepoint output vector which considers the change of engine total inlet temperature is proposed to improve engine model accuracy especially the steady. The online feedback correction of measurable parameters is adopted to improve the steady and dynamic accuracy of model. The modeling errors of improved compact propulsion system dynamic model remain unchanged when engine total inlet temperature of different conditions are the same or changes small. The model accuracy of compact propulsion system dynamic model, especially the measurable parameters, is improved by online feedback correction. Moreover, the real-time performance of compact propulsion system dynamic model and improved compact propulsion system dynamic model are much better than component level model.

Section: Introductionmentioning

confidence: 99%

Aero-engine dynamic model based on an improved compact propulsion system dynamic model

Proceedings of the Institution of Mechanical Engineers, Part I:

Wang

Jin

et al. 2021

Self Cite

“…Therefore, it is necessary to carry out the integrated control for them. 3,4 Under this circumstance, the performance-seeking control (PSC) is proposed and researched by the National Aeronautics and Space Administration (NASA) in the 1990s. 5,6 The main purpose of PSC is to seek some kinds of performances, such as maximum thrust, minimum specific fuel consumption, and minimum turbine inlet temperature during different flight missions.…”

Section: Introductionmentioning

confidence: 99%

A study on global optimization and deep neural network modeling method in performance-seeking control

Proceedings of the Institution of Mechanical Engineers, Part I:

Wang

et al. 2019

Self Cite

In this article, a novel performance-seeking control method based on deep neural network and interval analysis is proposed to obtain a better engine performance. A deep neural network modeling method which has stronger representation capability than conventional neural network and can deal with big training data is adopted to establish an on-board model in the subsonic and supersonic cruising envelops. Meanwhile, a global optimization algorithm interval analysis is applied here to get a better engine performance. Finally, two simulation experiments are conducted to verify the effectiveness of the proposed methods. One is the on-board model modeling which compares the deep neural network with the conventional neural network, and the other is the performance-seeking control simulations comparing interval analysis with feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm, respectively. These two experiments show that the deep neural network has much higher precision than the conventional neural network and the interval analysis gets much better engine performance than feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm.

“…1 The traditional engine control system is a sensor-based control method, that is, through controlling the measurable parameters such as engine pressure ratio (EPR), rotor speed, or other measurable parameters to control engine thrust. 2,3 However, due to degradation and manufacture and location tolerance, the relationship between the measurable parameters and the thrust will be changed. Hence, the control errors of thrust are inevitable if the traditional control system is adopted.…”

Section: Introductionmentioning

confidence: 99%

Aero-engine direct thrust control with nonlinear model predictive control based on linearized deep neural network predictor

Proceedings of the Institution of Mechanical Engineers, Part I:

Yong

Sun

et al. 2019

Self Cite

A novel nonlinear model predictive control method for aero-engine direct thrust control is proposed to improve engine response ability and reduce computational complexity of nonlinear model predictive control. The control objective of the proposed method is the thrust directly instead of the measurable parameters. The linearized model based on online sliding window deep neural network is proposed as predictive model. The online sliding window deep neural network has strong fitting capacity for nonlinear object and adopted to fitting the transient process of engine. The back propagation is adopted to obtain linearized model of online sliding window deep neural network, which greatly reduce the calculated amount. The comparison simulations of the popular nonlinear model predictive control based on extended Kalman filter and the proposed one are carried out. The simulation results show that compared with the popular nonlinear model predictive control, the proposed nonlinear model predictive control not only has the better response ability but also has reduced computational complexity greatly, nearly reduce computation time more than 35 ms.