scholarly journals Adaptive Fuzzy Neural Network PID Algorithm for BLDCM Speed Control System

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 118
Author(s):  
Hongqiao Yin ◽  
Wenjun Yi ◽  
Jintao Wu ◽  
Kangjian Wang ◽  
Jun Guan
Keyword(s):  
Neural Network ◽  
Control System ◽  
Pid Control ◽  
Simple Structure ◽  
High Efficiency ◽  
High Reliability ◽  
Low Noise ◽  
Fuzzy Pid ◽  
Control Effect ◽  
Pid Algorithm

Because of its simple structure, high efficiency, low noise, and high reliability, the brushless direct current motor (BLDCM) has an irreplaceable role compared with other types of motors in many aspects. The traditional proportional integral derivative (PID) control algorithm has been widely used in practical engineering because of its simple structure and convenient adjustment, but it has many shortcomings in control accuracy and other aspects. Therefore, in this paper, a fuzzy single neuron neural network (FSNNN) PID algorithm based on an automatic speed regulator (ASR) is designed and applied to a BLDCM control system. This paper introduces a BLDCM mathematical model and its control system and designs an FSNNN PID algorithm that takes speed deviation e at different sampling times as inputs of a neural network to adjust the PID parameters, and then it uses a fuzzy system to adjust gain K of the neural network. In addition, the frequency domain stability of a double closed loop PID control system is analyzed, and the control effect of traditional PID, fuzzy PID, and FSNNN PID algorithms are compared by setting different reference speeds, as well as the change rules of three-phase current, back electromotive force (EMF), electromagnetic torque, and rotor angle position. Finally, results show that a motor controlled by the FSNNN PID algorithm has certain superiority compared with traditional PID and fuzzy PID algorithms and also has better control effects.

WNN Optimizing PID Controller for BLDC Control System

2013 ◽  
Vol 278-280 ◽  
pp. 1529-1532
Author(s):  
Hong Pei Han ◽  
Wu Wang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Pid Control ◽  
Pid Controller ◽  
High Efficiency ◽  
Good Control ◽  
Industrial Applications ◽  
Wavelet Neural Network ◽  
Control Effect ◽  
Pid Tuning

Brushless DC motors (BLDC) are widely used for many industrial applications because of their high efficiency, high torque and low volume. This paper presents the PID control for BLDC Motor, because good control effect cannot be acquired by using the traditional PID control in the non-linear variable time servomechanism and it is difficult to tune the parameters and get satisfied control characteristics, some intelligent techniques should be taken. Wavelet Neural Network (WNN) was constrictive and fluctuant of wavelet transform and has self-study, self adjustment and nonlinear mapping functions of neural networks, So, a wavelet neural network self-tuning proportional-integral-derivative (PID) controller was proposed. The structure of WNN and PID tuning with WNN was presented and the equivalent circuit of BLDC and its mathematical models was analyzed, the simulation was taken with new method, the efficiency and advantages of this control strategy was successfully demonstrated which can applied into BLDC control system.

The Research and Design of Liquid Level Control System Based on Computer Nerve Network

2012 ◽  
Vol 591-593 ◽  
pp. 1629-1632
Author(s):  
Li Zhang ◽  
Jian Hui Wang ◽  
Hou Yao Zhu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Pid Control ◽  
Liquid Level ◽  
Level Control ◽  
Control Effect ◽  
Nerve Network ◽  
Level Control System ◽  
Pid Neural Network ◽  
Liquid Level Control

This thesis mainly elaborated the PID neural network feed-forward algo-rithm and back propagation algorithm and the structure form of its controller, then make use of MATLAB to simulate the liquid level adjusting system, analysis its control perform-ance and choose appropriate neural network parameters, and compared with the traditional PID control effect, analyzes the advantages of PID neural network. Through the comparison with the conventional PID control, PID neural network is superior to the traditional PID. The traditional PID control tuning parameters has a large number of thumb rules for reference, but the setting out of the parameters is not necessarily good. And sometimes we have to modify the parameters if we wound the better control effect. PID neural network is set up as long as the learning step in accordance with the PID rule set. this paper has show that Liquid Level Control System based on Computer Nerve Network has good control effect of rapid and effective.

Application of Compound PID Control in the DC Servo Motor

2009 ◽  
Vol 16-19 ◽  
pp. 145-149 ◽  
Author(s):  
Xiao Yan Song ◽  
Qing Jie Yang ◽  
Xue Ming Zhang ◽  
Qi Gao Feng
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Pid Control ◽  
Pid Controller ◽  
Design Method ◽  
Servo System ◽  
Fuzzy Pid ◽  
Control Effect ◽  
Fuzzy Pid Controller ◽  
Performance Requirements

Although the traditional PID controller is widely used in many fields, the system parameters varying and external disturbances existing in the DC servo system will cause large overshoot or poor stability. To improve the performance of the PID controller, a compound servo control system combining the conventional PID control and the fuzzy control is presented to meet the demand of a vehicular antenna servo system in this paper. Incorporating the fuzzy control and the conventional PID control, this paper presents a design method of the fuzzy PID controller that is based on the fuzzy tuning rules and formed by integrating two above control ideas. Simulation results are presented to show the efficiency of the proposed controller. The practical control effect shows that the control system that adopts the fuzzy PID controller has better performance than that of the traditional PID control system, and meets the performance requirements of the servo system.

Fluidized Bed Temperature Control System Based on Virtual Instrument Technology and Fuzzy PID Algorithm

2012 ◽  
Vol 562-564 ◽  
pp. 1594-1597
Author(s):  
Chun Qia Liu ◽  
Shi Feng Yang
Keyword(s):  
Control System ◽  
Fluidized Bed ◽  
Temperature Control ◽  
Virtual Instrument ◽  
High Reliability ◽  
Temperature Control System ◽  
Fuzzy Pid ◽  
Furnace Temperature ◽  
Pid Algorithm ◽  
Upper Computer

The fluidized bed is a complex system with a big lag, time-varying and non-linear. The conventional-PID methods are simple, practical, and high reliability. However, choosing and adjusting PID parameters rely on manual way. It is difficult to choose appropriate values when temperature requirement is higher. Inappropriate values may cause large overshoot and low control precision. Thus, in order to obtain more accurate and rapid PID control parameters and to avoid errors caused by human factors, the fuzzy control and PID algorithm were applied to the fluidized bed furnace temperature control system. The Fuzzy-PID controller was designed and the three PID parameters' self-tuning was realized. Simultaneously, the upper computer and the lower computer were designed. The lower computer mainly completed temperature measurement and adjustment functions. The collected temperature was transferred back to the upper computer at regular intervals. The upper computer was designed by virtual instrument technology. Practical operation shows that the temperature variation is below 0.3 when heating oven is in stable state and is close to the ideal PID response curve, which meets the average requirements of the fluidized bed heating oven. As an advanced reactor, fluidized bed was widely used in industrial process such as combustion, gasification and catalytic cracking[1].As the temperature affect the gas product composition of the fluidized bed, so improving the furnace temperature utilizing the automatic control system is one of the important issues furnace. The fluidized bed heating oven is heated by resistance wire heating and cooled by natural cooling. The temperature control after the adjustment is slow. It is a complex system with a big lag, time-varying and non-linear. Currently, the conventional-PID methods were taken to control the fluidized bed heating oven's temperature. This method is simple, practical, and high reliability. However, choosing and adjusting PID parameter rely on manual way, it is difficult to choose an appropriate values .Inappropriate values may cause large overshoot and low control precision. Thus, in order to obtain more accurate and rapid PID control parameters and to avoid errors caused by human factors, the fuzzy control and PID algorithm are applied to the fluidized bed furnace temperature control system. The self-tuning fuzzy PID controller is designed. Compared with the outdated control methods, PC control is more flexible and even more long-range.

Research on Application of Fuzzy PID Control Based on PLC in Hot Press Control System

2014 ◽  
Vol 1006-1007 ◽  
pp. 689-692
Author(s):  
Jing Jie Ma ◽  
Xiao Li Zhang ◽  
He Ying Bian
Keyword(s):  
Control System ◽  
Pid Control ◽  
Fuzzy Pid ◽  
Hot Press ◽  
Technical Process ◽  
Control Effect ◽  
Fuzzy Pid Control ◽  
System Experiment ◽  
Simulation Results ◽  
Research On Application

This research constructed a hot press control system according to the hot-press technical process of wood-based panel, and applied the fuzzy control and traditional PID control to this system. Experiment and simulation results showed that the control effect of the new method is optimal.

Annular Heating Furnace Temperature Control Based on the Fuzzy-PID Algorithm and Cascade Double-Cross Limiting Control

2014 ◽  
Vol 556-562 ◽  
pp. 2478-2482 ◽  
Author(s):  
Ting Gui Li ◽  
Gang Li
Keyword(s):  
Temperature Control ◽  
Pid Control ◽  
Industrial Process ◽  
Heating Furnace ◽  
Fuzzy Pid ◽  
Control Effect ◽  
Fuzzy Pid Control ◽  
Furnace Temperature ◽  
Pid Algorithm ◽  
Double Cross

Annular heating furnace temperature control is a typical and complex industrial process control system,with the characteristics of multivariable,time-varying parameters,nonlinear,coupling, large inertia and pure delay,so it is difficult to obtain satisfactory control effect when using conventional PID control. In view of the above problems, adopting fuzzy PID algorithm and cascade double-cross limiting control, combustion control effect is greatly improved, self-tuning of PID parameters is realized, and control quality is enhanced. The experimental results show that, fuzzy PID control shows good dynamic and steady performance, its control effect is significantly improved when compared with conventional PID control, and it has better overall performance.

Optimization of the Hydraulic Control System Utilizing BP Neural Network Control Strategy Based on Genetic Algorithm

2013 ◽  
Vol 397-400 ◽  
pp. 1245-1252
Author(s):  
Ying Ying Feng ◽  
Nan Mu Hui ◽  
Zong An Luo ◽  
Dian Hua Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Control ◽  
Hydraulic System ◽  
Learning Ability ◽  
Hydraulic Control ◽  
Control Effect ◽  
Hydraulic Control System

For the characteristic of the MMS series Thermo-Mechanical Simulator hydraulic control system, using traditional PID control method can not achieve the desired control effect. Basing on genetic algorithm, BP neural network, which has the arbitrary non-linear approximation ability, self-learning ability and generalization ability, has been used into the hydraulic control system to achieve the online adjustment of the weighting coefficients and the adaptive adjustment of PID control parameters. The results of simulation and online tests show that the control effect of hydraulic system has been improved significantly, and the accurate control of hydraulic system hammer displacement has been realized.

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