Speed-Regulation System of PMLSM Based on BP Neural Network PID Control

2012 ◽  
Vol 466-467 ◽  
pp. 1217-1221 ◽  
Author(s):  
Fu Zhong Wang ◽  
Qiong Xia Yu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Dynamic Performance ◽  
Regulation System ◽  
Time Varying ◽  
Bp Network ◽  
Speed Controller ◽  
Speed Regulation ◽  
Neural Network Pid

For the characteristics of permanent magnet linear synchronous motor (PMLSM) hoisting system’s nonlinear, time-varying and vulnerable to disturbance, based on the established PMLSM d-q axis dynamic model, designed of an improved BP neural network PID speed controller. Modified the fixed learning rate in BP neural network to adaptive adjustable, and added the momentum to reduce the oscillation tendency in the learning process, greatly improved the convergence speed and avoided the network into a local minimum. Compared with the simulations of traditional PID and the improved BP network PID speed controller, the results showed that the improved BP neural network PID speed controller had the high quality and it can make the system with better dynamic performance and robustness.

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.

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

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.

Application of BP Neural Network PID Controller in Landing Gear Based on MRF Damper

2015 ◽  
Vol 779 ◽  
pp. 226-232 ◽  
Author(s):  
Shi Xing Zhu ◽  
Yue Han ◽  
Bo Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Experimental Testing ◽  
Computation Time ◽  
Landing Gear ◽  
Time Varying ◽  
Control Effect ◽  
Simulation And Experiment ◽  
Neural Network Pid

For characteristics of nonlinearity and time-varying volatility of landing gear based on MR damper, a BP neural network PID controller with a momentum was designed on basis of established dynamic mathematical model. BP neural network would adjust three parameters of PID online in time. The controller was inputted the energy which was combined by the feedback of acceleration and displacement of the control system, which greatly reduced the computation time of controller and the control effect was more obvious. After compared with PID, the simulation and experiment have showed that BP neural network PID has a better effect. The arithmetic can be put into practice through experimental testing.

The Application of PSO-BP Neural Network PID Controller in Variable Frequency Speed Regulation System

2014 ◽  
Vol 599-601 ◽  
pp. 1090-1093 ◽  
Author(s):  
Chun Hua Li ◽  
Shao Xiong Xu ◽  
Yang Xie ◽  
Jie Zhao
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Speed Control ◽  
Regulation System ◽  
Particle Swarm Algorithm ◽  
Variable Frequency ◽  
Speed Control System ◽  
Neural Network Pid

Variable frequency speed control system hold good stability,more efficient,more energy conservation etc, so it has been widely used in the industrial areas,but the control strategy of traditional was difficult to achieve the desired control effect.This paper adopt particle swarm algorithm and BP neural network to construct the PID controller of PSO-BP neural network , the M-Files of PSO-BP neural network PID based on MATLAB through S-Function, and the mode of PSO-BP neural network PID variable frequency speed control system was established in SIMULINK platform.Simulation results show that the controller hold well robustness, follow and stability,and the dynamic characteristics of the original system was improved, the application value of this method in the variable frequency speed control system was proved.

Study for PID Temperature Control of Plant Growth Cabinet Based on Neural Network

2015 ◽  
Vol 734 ◽  
pp. 229-232
Author(s):  
Zhi Qi Liu ◽  
Li Guo Tian ◽  
Meng Li ◽  
Jiang Lin Wei ◽  
Gao Li Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Plant Growth ◽  
Bp Neural Network ◽  
Pid Control ◽  
Response Speed ◽  
Fast Response ◽  
Control Methods ◽  
Target Temperature ◽  
Growth Environment ◽  
Neural Network Pid

Environment for plant growth is difficult to establish precise mathematical model. The conventional control methods are difficult to be well controlled, and put forward a neural network PID control temperature on the growth environment of plant. In this paper, taking the lettuce as an example, using MATLAB to simulate the PID control and PID control of BP neural network, the results proved that PID control of BP neural network has small overshoot, fast response speed and good stability compared with the traditional PID control, and better controlled temperature changing with the target temperature.

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.

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