A Deep Neural Network Based Predictive Control Strategy for High Frequency Multilevel Converters

2018 ◽  
Author(s):  
Daming Wang ◽  
Xin Yin ◽  
Sai Tang ◽  
Chao Zhang ◽  
Z. John Shen ◽  
...  
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Control Strategy ◽  
High Frequency ◽  
Deep Neural Network ◽  
Multilevel Converters

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

2019 ◽  
Vol 234 (3) ◽  
pp. 330-337 ◽  
Author(s):  
Qiangang Zheng ◽  
Yong Wang ◽  
Fengyong Sun ◽  
Haibo Zhang
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Deep Neural Network ◽  
Sliding Window ◽  
Nonlinear Model Predictive Control ◽  
Thrust Control ◽  
Direct Thrust Control ◽  
Linearized Model

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.

Predictive Control of DC/DC Converter Based on State Observer

2012 ◽  
Vol 476-478 ◽  
pp. 542-546 ◽  
Author(s):  
Chen Zeng ◽  
Deng Min Pan ◽  
Li Yan Zhang
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Control Strategy ◽  
Network Optimization ◽  
State Observer ◽  
Single Sample ◽  
Neural Network Optimization ◽  
Current State ◽  
Simulation Results ◽  
Control State

In this paper, we present an advanced way to control the DC/DC converter by using predictive control. As the current state of the circuit must be known while using the predictive control, state observer is applied to solve the problem that some variables of DC/DC converter can not be observed. Neural network optimization is used to solve the QP problems in single sample step of predictive control. Simulation results show that this approach can utilize fast converge property of neural network and the new control strategy turns out to be very efficiency.

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