A turboshaft engine NMPC scheme for helicopter autorotation recovery maneuver

Proceedings of the Institution of Mechanical Engineers, Part G:

Chen

Wang

et al. 2020

Self Cite

A novel performance seeking control method based on hybrid optimization algorithm and deep learning modeling method is proposed to get a better engine performance. The deep learning modeling method, deep neural network, which has strong representation capability and can deal with big training data, is adopted to establish an on-board engine model. A hybrid optimization algorithm—genetic algorithm particle swarm optimization–feasible sequential quadratic programming—is proposed and applied to performance seeking control. The genetic algorithm particle swarm optimization–feasible sequential quadratic programming not only has the global search ability of genetic algorithm particle swarm optimization, but also has the high local search accuracy of feasible sequential quadratic programming. The final simulation experiments show that, compared with feasible sequential quadratic programming, genetic algorithm particle swarm optimization, and genetic algorithm, the proposed optimization algorithm can get more installed thrust, decrease fuel consumption between 2% to 3%, and decrease turbine blade temperature larger than 15k, while meeting all of the constraints. Moreover, it also shows that the proposed modeling method has high accuracy and real-time performance.

Section: Introductionmentioning

confidence: 99%

Research on hybrid optimization and deep learning modeling method in the performance seeking control

Proceedings of the Institution of Mechanical Engineers, Part G:

Chen

Wang

et al. 2020

Self Cite

“…The CLM has better accuracy, but it has poor real-time performance. Therefore, many scholars proposed support vector machine (SVM) [19][20][21] and neural network (NN) 22,23 for engine modeling. The accuracy and real time of these two methods are a compromise between the PLM and the CLM.…”

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.

“…The MPC, 5 which systematically addresses the problem of multiple objectives with constraints on engine inputs and states, has the better transient performance than the traditional controller and has aroused many aero-engine control researchers interest. 8–19 Van Essen et al 10,11 implement nonlinear model predictive control (NMPC) to a laboratory gas turbine installation and assess the feasibility (stability, real-time performance, robustness) of NMPC. Brunell et al 12 investigate the feasibility of constrained NMPC with simplified real-time model and apply it to a component-level model of turbojet engine.…”

Section: Introductionmentioning

confidence: 99%

“…16,17 The NMPC based on state variable model (SVM) is proposed and applied to integrated variable speed control of helicopter and turboshaft engine. 18 Zheng et al 19 applied NMPC for helicopter autorotation recovery maneuver to obtain better engine response speed and reduce the rotor transient droop during autorotation power recovery. The predictive models of above works are main focus on linear model especially on extended Kalman filter (EKF).…”

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.