Research and Design of Five-Axis Balanced Camera Stabilizer Based on BP Neural Network PID Algorithm
Pages 431 - 436
Abstract
This paper adaptively adjusts the three parameters of proportional, integral and differential for five-axis balanced camera stabilizer. Real-time adjustment of the input of the controlled object, so that the system responds quickly and stabilizes, thereby reducing the blur caused by external factors such as jitter caused by the camera's captured image. Enables a handheld camera stabilizer to take a clear picture while the photographer's arm shakes. In order to improve the stability of the triaxial hand-held camera, the wobble of the up and down motion cannot be eliminated. Two mechanical anti-jitter shaft arms are loaded under the triaxial stabilizer to make it a five-axis stabilizer, which can keep the camera picture stable in the pitching roll course and five directions of movement above and below. Through the corresponding simulation experiment, it is verified that the control of PID controller through BP neural network can make the system have high accuracy and strong stability, maintain the balance of the picture, and obtain accurate control effect for the stable position of the camera.
References
[1]
Chen Guilin,Ma Sheng,Guo Yang. Review of Hardware Accelerating Neural Networks[J]. Journal of Computer Research and Development,2019,56(02):240--253.
[2]
L Ma, Y Yao, M Wang. The Optimizing Design of Wheeled Robot Tracking System by PID Control Algorithm Based on BP Neural Network[C] IEEE International Conference on Industrial Informatics-computing Technology, 2017:34--39.
[3]
Li Huifeng. Hypersonic vehicle guidance and control technology[M]. Beijing: China Aerospace Publishing House,2012:54--85.
[4]
Guan ping, He Zhiwei, Ge Xinsheng. Neural network PID control of hypersonic vehicle [J]. Aerospace control, 2018,36(2):15--17.
[5]
Zhang Yongzhen, Su Hansong, Liu Gaohua,et al. Parameter adjustment of PID controller based on BP neural network[J]Journal of Nankai University(Natural Science).2018(03):26--30.
[6]
Fu Xiaoming, Wang Fulin, Shang Jiajie. Optimization of BP Neural Network Based on Multi-child Genetic Algorithm[J]. Computer Simulation. 2016(03):23--27.
[7]
LI Xianhong, YU Hai-bin, Zeng Peng, et al. Design method of saturation optimal PID controller with delay in low-order controlled process[J]. Information and Control,2015(01):43--50.
[8]
Gao Fuqiang,Li Ping,Zhang Leimin, et al. PID control and simulation based on BP neural network tuning[J]. Shandong Ceramics 2017,40(03):27--31.
[9]
Yuan Dong, MA Xiaojun, Zeng Qinghan, et al. Study on frequency band characteristics and parameter configuration oflinear auto-disturbance rejection controller for second- order systems[J]. Control Theory & Applications, 2013,30(12):1630--1637.
[10]
Tian Wei, Peng Yizhun, Wang Pan, et al. A application of BP neural network PID algorithm in refrigerated space temperature[J]. Applied Mechanics and Materials, 2014,(599): 827--830.
[11]
Liu Bingyan, Zhu Wu, Zhang Jiamin. Study on self-tuning PID constant temperature control system based on BP neural network.[J] Instrument Technology and Sensors. 2018 (0-8): 52--55.
[12]
TOMAS D, et al. A highly flexible reconfigurable system on a Xilinx FPGA[C]. Cancun: 2014 International Conference on ReConFigurable Computing and FPGAs, 2014.
[13]
Mo Meng, Zhao Longzhang, Gong Yuwen, Wu Yang. Research and Application of BP Neural Network Based on Genetic Algorithm Optimization[J]. Modern electronic technology. 2018(09):41--44.
Index Terms
- Research and Design of Five-Axis Balanced Camera Stabilizer Based on BP Neural Network PID Algorithm
Recommendations
Research on the Application of PID Control with Neural Network and Parameter Adjustment Method of PID Controller
CSAI '18: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial IntelligencePID control is a kind of control method based on the error of the system, using the proportion, integral and differential to calculate the control quantity and adjust the system error. The PID controller is widely used in various fields of industrial ...
Research on PID Control Algorithm of BP Network based on Two Axis Digital Control Turntable
CIT '10: Proceedings of the 2010 10th IEEE International Conference on Computer and Information TechnologyUnder the research context of two axis digital control turntable, this paper has set up a mathematical model of control system of turntables. Proceeding from such model, it has made an analysis on the deficiencies of classical PID control during ...
Application of Self-Tuning of PID Control Based on BP Neural Networks in the Mobile Robot Target Tracking
IMCCC '13: Proceedings of the 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and ControlArtificial neural network (ANN), also known as parallel distributed processing model or connection mechanism model, is an information processing system or a computer system based on the structure and the ability to mimic the human brain [1]. BP neural ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
November 2019
528 pages
ISBN:9781450361910
DOI:10.1145/3378065
Copyright © 2019 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- Guilin: Guilin University of Technology, Guilin, China
- Wuhan University of Technology: Wuhan University of Technology, Wuhan, China
- International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 08 April 2020
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICIIP 2019
ICIIP 2019: 2019 4th International Conference on Intelligent Information Processing
November 16 - 17, 2019
China, China
Acceptance Rates
Overall Acceptance Rate 87 of 367 submissions, 24%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 52Total Downloads
- Downloads (Last 12 months)3
- Downloads (Last 6 weeks)0
Reflects downloads up to 21 Nov 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in