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.

scholarly journals An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

2021 ◽  
Vol 11 (6) ◽  
pp. 2685
Author(s):  
Guojin Pei ◽  
Ming Yu ◽  
Yaohui Xu ◽  
Cui Ma ◽  
Houhu Lai ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Experimental Validation ◽  
Control Method ◽  
Smith Predictor ◽  
Constant Force ◽  
Matlab Simulation ◽  
Adjustment Time

A compliant constant-force actuator based on the cylinder is an important tool for the contact operation of robots. Due to the nonlinearity and time delay of the pneumatic system, the traditional proportional–integral–derivative (PID) method for constant force control does not work so well. In this paper, an improved PID control method combining a backpropagation (BP) neural network and the Smith predictor is proposed. Through MATLAB simulation and experimental validation, the results show that the proposed method can shorten the maximum overshoot and the adjustment time compared with traditional the PID method.

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.

Study on Improved Neural Network PID Control for Coal Mining Power Grid Arc Suppression Coil Compensating System

2010 ◽  
Vol 426-427 ◽  
pp. 427-431
Author(s):  
C.Y. Ma ◽  
C.L. Wang ◽  
J.H. Liu ◽  
X.B. Li ◽  
R. Liang
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Bp Neural Network ◽  
Pid Control ◽  
Model Building ◽  
Control Method ◽  
Control Effect ◽  
Loop Control ◽  
Arc Suppression Coil ◽  
Neural Network Pid

The paper analyzed arc suppression coil with magnetic bias compensating system with linear system rules. The nonlinear and time-variable performances are considered during model building process. In order to optimize control effect, the paper adopted improved BP neural network PID controller with closed loop control method. Improve BP neural network with the combination of the two strategies, adding momentum method and adaptive learning rate adjustment, can not only effectively suppress the network appearing local minimum but also good to shorten learning time and improve stability of the network furthermore. The results of simulation and experiments indicate that arc suppression coil based on improved neural network with PID control method can quickly and accurately control the compensating capacitive current to an expected value and it has strong robustness. The paper also provided core controller with software and hardware designing scheme based on STM32 microcontroller.

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 Control Strategy of Automatic Control System for Sewage Treatment

2013 ◽  
Vol 791-793 ◽  
pp. 690-693
Author(s):  
Zhang Hong ◽  
Xiao Liang Liu ◽  
Fang Wei
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Treatment Process ◽  
Sewage Treatment ◽  
Bp Algorithm ◽  
Dissolved Oxygen Concentration ◽  
Control Effect

For the characteristics of the sewage treatment process and a combination of BP algorithm and conventional PID control, a PID controller is proposed based on BP neural network to realize the online adjustment of PID controller parameters. This control strategy will be applied to the control of the DO(Dissolved Oxygen) concentration in sewage treatment, and a contrast has been made with conventional PID control effect.

A Study on PID Control and Simulation Based on BP Neural Network

2012 ◽  
Vol 468-471 ◽  
pp. 742-745
Author(s):  
Fang Fang Zhai ◽  
Shao Li Ma ◽  
Wei Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Control Method ◽  
Uncertain System ◽  
Real Time Control ◽  
Time Control ◽  
The Neural Network ◽  
Neural Network Pid

This paper introduces the neural network PID control method, in which the parameters of PID controller is adjusted by the use of the self-study ability. And the PID controller can adapt itself actively. The dynamic BP algorithm of the three-layered network realizes the online real-time control, which displays the robustness of the PID control, and the capability of BP neural network to deal with nonlinear and uncertain system. A simulation is made by using of this method. The result of it shows that the neural network PID controller is better than the conventional one, and has higher accuracy and stronger adaptability, which can get the satisfied control result.

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.

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