scholarly journals LQR optimized BP neural network PI controller for speed control of brushless DC motor

2020 ◽  
Vol 12 (10) ◽  
pp. 168781402096898
Author(s):  
Tingting Wang ◽  
Hongzhi Wang ◽  
Huangshui Hu ◽  
Chuhang Wang
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Linear Quadratic Regulator ◽  
Speed Control ◽  
Response Speed ◽  
Back Propagation Neural Network ◽  
Pi Controller ◽  
Bldc Motor ◽  
Linear Quadratic ◽  
Brushless Dc

This paper proposes a linear quadratic regulator (LQR) optimized back propagation neural network (BPNN) PI controller called LN-PI for the speed control of brushless direct current (BLDC) motor. The controller adopts BPNN to adjust the gain [Formula: see text] and [Formula: see text] of PI, which improves the dynamic characteristics and robustness of the controller. Moreover, LQR is adopted to optimize the output of BPNN so as to make it close to the target PI gains. Finally, the optimized control output is inputted into the BLDC motor system to achieve speed control. The performance analysis of the proposed controller is presented to compare with traditional PI controller, neural network PI controller and LQR optimized PI controller under MATLAB/Simulink, the results shows that the proposed controller effectively improves the response speed, reduces the steady-state error and enhances the anti-interference ability.

scholarly journals Speed Control of BLDC Motor using Open Loop, PID Controller and Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 2210-2213
Keyword(s):  
Neural Network ◽  
Simulation Study ◽  
Pid Controller ◽  
Speed Control ◽  
Open Loop ◽  
Bldc Motor ◽  
Variable Speed ◽  
Brushless Dc Motors ◽  
Brushless Dc ◽  
Dc Motors

is main goal of upcoming and present applications. However, its possible to achive these aims using brushless DC motors (BLDC), due to its use in many applications. The applications such as sppining, drilling, elevators, lathes, etc can be exicuted using BLDC motor and can replace conventional DC brush motor. The effective vechiel control required for applications of variable speed can be achived using BLDC motors. This paper presents speed control of BLDC motor for open loop using PID and neural network techniques and their comparative study. From the simulation study it is observed that neural network gives better performance compaiered to other technique.

DSP-Based on Brushless DC Motor Speed Control by PI Controller Using Back EMF Detection

2015 ◽  
Vol 763 ◽  
pp. 63-70
Author(s):  
Danupon Kumpanya ◽  
Chookiat Kiree ◽  
Satean Tunyasrirut ◽  
Deacha Puangdownreong
Keyword(s):  
Speed Control ◽  
Pi Controller ◽  
Pole Placement ◽  
Brushless Dc Motor ◽  
Bldc Motor ◽  
Motor Speed ◽  
Brushless Dc ◽  
Speed Adjustment ◽  
Placement Technique ◽  
Speed Response

This paper presents a design and implementation of Brushless DC (BLDC) motor speed control based on the TMS320F28335 DSP board interfacing to MATLAB/SIMULINK. To obtain the optimal tracking and regulating responses, the PI controller is conducted and designed by pole placement technique. With Back EMF detection, the proposed system is considered as a class of sensorless control. This scheme leads to the speed adjustment of the BLDC motor by PWM. This experiment aims to examine the effectiveness of BLDC motor by testing the BLDC motor of 100 watt. It was found that the speed response of BLDC motor can be regulated at the operating speed of 700 rpm and 1400 rpm at no load and full load conditions.

Application of optimal current driver for the torque control of BLDC motor

2011 ◽  
Vol 60 (2) ◽  
pp. 149-158 ◽  
Author(s):  
Jakub Bernat ◽  
Sławomir Stępień
Keyword(s):  
Feedback Linearization ◽  
Fem Analysis ◽  
Linear Quadratic Regulator ◽  
Torque Control ◽  
Brushless Dc Motor ◽  
The Novel ◽  
Bldc Motor ◽  
Linear Quadratic ◽  
Brushless Dc ◽  
Optimal Current

Application of optimal current driver for the torque control of BLDC motorThis research presents the novel control strategy of the brushless DC motor. The optimal current driver is designed using Linear Quadratic Regulator and feedback linearization. Additionally, the current reshaping strategy is applied to control the motor torque. Thus, the torque controller is built based on the optimal current driver. The motor is simulated using the FEM analysis.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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