Neural network-based model predictive control with fuzzy-SQP optimization for direct thrust control of turbofan engine

Wang, Yangjing; Huang, Jinquan; Zhou, Wenxiang; Lü, Feng; Xu, Wenhao

doi:10.1016/j.cja.2022.04.012

Cited by 13 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…See Figure 1 for an illustration. The BP neural network training process is as follows [10] . We suppose that there are A training samples to train the network, one of which is a.…”

Section: Bp Neural Networkmentioning

confidence: 99%

Corporate Performance Prediction Based on BP Neural Network

Ye¹

2023

Proceedings of the 2nd International Conference on Big Data Economy and Digital Management, BDEDM 2023, January 6-8, 2023, Chan

View full text Add to dashboard Cite

Top Management Team (TMT), as the most important presence in corporate decision-making, is an integral part of corporate research. Its impact on corporate performance is a topic that cannot be ignored. This paper uses earnings per share (EPS) to represent corporate performance, and the shareholding ratio of executives (SHARE) is used as TMT characteristics. Based on the BP neural network, the input layer of the model is set as five nodes, the implied layer as two nodes, and the output layer as one node. We use 75% of the data of listed information technology companies from 2017-2019 as the training set to derive the performance prediction model. In this study, 25% of the test set is used to validate the final valid performance prediction model obtained. This study integrates TMT characteristics into predicting corporate performance, helping to optimize the non-economic indicators used to assess and predict corporate performance.

show abstract

“…See Figure 1 for an illustration. The BP neural network training process is as follows [10] . We suppose that there are A training samples to train the network, one of which is a.…”

Section: Bp Neural Networkmentioning

confidence: 99%

Corporate Performance Prediction Based on BP Neural Network

Ye¹

2023

Proceedings of the 2nd International Conference on Big Data Economy and Digital Management, BDEDM 2023, January 6-8, 2023, Chan

View full text Add to dashboard Cite

show abstract

“…The constraints consider the physical limitations of the actuators, whereas other techniques (e.g., anti-windup) require additional design to handle them [11]. Therefore, researchers try to apply MPC to aero-engines [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Reduced-Dimensional Scheme of Model Predictive Control for Aero-Engines

Jiang,

Wang,

Liu

et al. 2024

Actuators

View full text Add to dashboard Cite

Model Predictive Control (MPC) has many advantages in controlling an aero-engine, such as handling actuator constraints, but the computational burden greatly obstructs its application. The current multiplex MPC can reduce computational complexity, but it will significantly decrease the control performance. To guarantee real-time performance and good control performance simultaneously, an intelligent reduced-dimensional scheme of MPC is proposed. The scheme includes a control variable selection algorithm and a control sequence coordination strategy. A constrained optimization problem with low computational complexity is first constructed by using only one control variable to define a reduced-dimensional control sequence. Therein, the control variable selection algorithm provides an intelligent mode to determine the control variable that has the best control effect at the current sampling instant. Furthermore, a coordination strategy is adopted in the reduced-dimensional control sequence to consider the interaction of control variables at different predicting instants. Finally, an intelligent reduced-dimensional MPC controller is designed and implemented on an aero-engine. Simulation results demonstrate the effectiveness of the intelligent reduced-dimensional scheme. Compared with the multiplex MPC, the intelligent reduced-dimensional MPC controller enhances the control quality significantly by 34.06%; compared with the standard MPC, the average time consumption is decreased by 64.72%.

show abstract