scholarly journals A Novel PID Control Strategy Based on Improved GA-BP Neural Network for Phase-Shifted Full-Bridge Current-Doubler Synchronous Rectifying Converter

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hemiao Liu ◽  
Yanming Cheng ◽  
Yulian Zhao ◽  
Mahmoud Al Shurafa ◽  
Jing Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Control ◽  
Network Control ◽  
Maximum Efficiency ◽  
Neural Network Control ◽  
Control Performance ◽  
Wide Range ◽  
Neural Network Pid

In this paper, a phase-shifted full-bridge current-doubler synchronous rectifying converter (PSFB-CDSRC) based on IGBT and its control strategies are studied. In the main circuit, a current doubling synchronous rectifying circuit based on IGBT is presented to further reduce the power loss of power devices. Moreover, in the control strategy, in view of the existing researches, the basic BP neural network PID control performance of the rectifying converter still can be further improved. Therefore, this paper combines the quasi-Newton algorithm and traditional GA to propose an improved GA-BP (IGA-BP) neural network to further improve PID control performance. The simulation results demonstrate that the maximum efficiency of 5 V/500 A rectifying converter based on the proposed circuit scheme can reach 94.1%, and the rectifying converter has a good performance of excellent waveform and wide range of load. IGA-BP neural network PID control responds fast and reaches the stable state quickly in comparison with that controlled by the GA-BP neural network control strategy, and the steady-state time can be reduced by 10.5% through using IGA-BP neural network control strategy. This study can provide a valuable guidance and reference, not only in circuit scheme but also in the optimal PID control strategy for design of the high-efficiency DC/DC rectifying converter with higher power in the future.

The Study of BP Neural Network PID Control Based on the Cutting Chatter Suppression System

2014 ◽  
Vol 1030-1032 ◽  
pp. 1574-1577
Author(s):  
Dao Kun Zhang ◽  
Rui Huo ◽  
Shu Ying Li ◽  
Xing Ke Cui ◽  
Cui Ping Liu
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Control ◽  
Simulated Data ◽  
Chatter Suppression ◽  
Cutting Chatter ◽  
Adaptive Ability ◽  
Neural Network Pid ◽  
Suppression System

The intelligent control strategy of BP neural combined network with classical PID control is mainly studied and simulated. The advantages of the control strategy are discussed. Based on the simulated data, the BP neural network PID control has the stronger adaptive ability.

Research on PID Neural Network Control System of Temperature for Agricutural Film Unit

2013 ◽  
Vol 341-342 ◽  
pp. 694-699
Author(s):  
Yue Feng ◽  
Mei Xia Qiao ◽  
Shuai Zheng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Control Algorithm ◽  
Melting Process ◽  
Network Control ◽  
Heating Process ◽  
Neural Network Control ◽  
Proportional Integral ◽  
Pid Neural Network

The temperature of agricultural film unit affects the plastic film directly. Since unit heating process has the characters of time delay, nonlinear, time-varying and strong coupling. It is difficult to create a mathematical model structure of plastic melting process. Thus, temperature control is very difficult. This paper presents decoupling control strategy and corresponding control algorithm based on PID (proportional-Integral-differential) neural network. Proportional, integral, differential neurons form a three-layer neural network. This design gives full play to respective advantages of PID control and neural network, and takes advantage of BP neural network to establish the dynamic model of system.

Application of BP Neural Network PID Algorithm in Refrigerated Space Temperature Control

2014 ◽  
Vol 599-601 ◽  
pp. 827-830 ◽  
Author(s):  
Wei Tian ◽  
Yi Zhun Peng ◽  
Pan Wang ◽  
Xiao Yu Wang
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Bp Neural Network ◽  
Pid Control ◽  
Bp Algorithm ◽  
Pid Algorithm ◽  
Controller Performance ◽  
Neural Network Algorithm ◽  
Adaptive Ability ◽  
Neural Network Pid

Taking the temperature control of a refrigerated space as example, this paper designs a controller which is based on traditional PID operation and BP neural network algorithm. It has better steady-state precision and adaptive ability. Firstly, the article introduces the concepts of the refrigerated space, PID and BP algorithm. Then, the temperature control of refrigerated space is simulated in MATLAB. The PID parameters will be adjusted by simulation in BP Neural Network. The PID control parameters could be created real-time online, which makes the controller performance best.

Application of Blow-off Wind Tunnel Control Based on Genetic Algorithm Optimized BP-Neural Network PID Neural Network

2013 ◽  
Vol 310 ◽  
pp. 557-559 ◽  
Author(s):  
Li Ji ◽  
Xiao Fei Lian
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wind Tunnel ◽  
Bp Neural Network ◽  
Control Method ◽  
Network Control ◽  
Operating Conditions ◽  
Algorithm Optimization ◽  
Large Delay ◽  
Neural Network Pid

For a blow-off tunnel running, there is the large delay and lag issues. We build a mathematical model of the wind tunnel Mach number control by the test modeling method, then analyse the pros and cons of various control methods based on BP neural network control algorithm. Put forward genetic algorithm optimization neural network adaptive control method to solve the large inertia of the wind tunnel system, and large delay. A large number of simulation studies, run a variety of operating conditions for the wind tunnel simulation proved that the improved adaptive neural network PID control method is reasonable and effective.

scholarly journals Research on Environmental Adjustment of Cloud Ranch Based on BP Neural Network PID Control

2018 ◽  
Vol 108 ◽  
pp. 042056
Author(s):  
Jinzhi Ren ◽  
Wei Xiang ◽  
Lin Zhao ◽  
Jianbo Wu ◽  
Lianzhen Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Neural Network Pid

scholarly journals Research on load simulator control strategy based on BP neural network and PID method

2020 ◽  
Vol 306 ◽  
pp. 03002
Author(s):  
Yong Zhou ◽  
Yubo Zhang ◽  
Tianhao Yang
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Control ◽  
Control Method ◽  
Vector Method ◽  
Network Learning ◽  
Learning Speed ◽  
Speed Up ◽  
Load Simulator

In the research of load simulator control method, PID control is the most widely used control strategy, but PID controller’s three parameters is difficult to set. This paper proposes a BP neural network feedforward PID controller system which uses BP neural network for setting these parameters, and in order to make the network learning speed up the convergence speed and not fall into local minimum, the adaptive vector method is adopted to improve the algorithm. The simulation and experimental results show that this method is good at avoiding the primeval shock and the sine tracking performance of the system has also been improved.

Research on the PID Control Strategy in BP Neural Network of Terrain Detector-Milling Cutting Depth System

2014 ◽  
Vol 945-949 ◽  
pp. 1573-1578
Author(s):  
Xiao Feng ◽  
Hao Hu ◽  
Fan Rang Kong ◽  
Shi Qiu ◽  
Ye Sun
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Control ◽  
Simulation Analysis ◽  
Dynamic Performance ◽  
Effective Control ◽  
Cutting Depth ◽  
Hydraulic Servo

Targeting at the nonlinear, time-varying characteristics of terrain detector-milling cutting depth electro-hydraulic servo system in soil milling collection machines, this paper proposed the PID control menthod in BP neural network of terrain detector - milling cutting depth system and designed PID controller in BP neural network and conducted simulation analysis by programming with Matlab. The results show that, when compared with conventional PID control, BP neural network compounded with PID control would enable the system better dynamic performance and follow-up characteristics, therefore, it is an effective control strategy.

Sign in / Sign up

Export Citation Format

Share Document