Intelligent Robot Motion Control System Based on Immune Genetic Algorithm

Article Preview

Abstract:

The robot's motion control system is the core technology of intelligent robot. In this paper based on immune genetic algorithm, we improve the intelligent robot control system, and design the intelligent robot action output system with adjustable ratio combined with the PID algorithm. The system has the adaptive adjustment function, by adjusting the proportional coefficient P, which can reduce the output error of the system, improve the adaptability of the system and accelerate the speed of motion control. Finally, we use Siemens S7-200 series products to simulate the action output, and obtain the output time and residual of action under different proportional coefficient P by simulation. It provides the technical reference for the research on control algorithm of intelligent robot.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

703-707

Citation:

Online since:

October 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Wang Kai. Intelligent sensor signal processing technology of AC voltage and current . Xihua University, 2010: 2-15.

Google Scholar

[2] Li Shixi. Coarse signal treatment of AC power intelligent sensor . Xihua University, 2010: 2-16.

Google Scholar

[3] Chen Shouqiang, Xiao Rongchuan, Xiao Jixue. Signal processing in power smart sensor . Sensor and micro system, 20011(11): 39-42.

Google Scholar

[4] Wang Xuewei, Hu Lingbin. Power measurement method using second generation wavelet transforms . Journal of measurement, 2012, 29(1): 73-76.

Google Scholar

[5] Zhuang Jian, Yang Qingyu, Du Haifeng, Yu Dehong. A highly complex system genetic algorithm . Journal of software, 2010, 21(11): 210-216.

Google Scholar

[6] Chen Shouwen, Li Mingdong. An improved genetic algorithm and its simulation . Computer applications and software, 2010, 27(9): 100-102.

Google Scholar

[7] Wang Kang, Yan Xuesong, Jin Jian, Zhan Zhigang. An improved genetic K clustering algorithm . Computer and digital engineering, 2010, 38(1): 18-20.

Google Scholar

[8] Yue Qian, Feng Shan. The calculation performance analysis of genetic algorithm . Chinese Journal of computers, 2012, 32(12): 26-30.

Google Scholar

[9] Su Xiaodong. Power quality online detection based on embedded Linux . Electrical measurement & instrumentation, 2011, 46(3): 32-37.

Google Scholar

[10] Shen Xiaofeng, Wang Jun, Zhu Wei, Feng Yuancheng. Power grid monitoring system based on virtual instrument technology . Low voltage electrical apparatus, 2011, 21(5): 12-16.

Google Scholar

[11] Li Binqin, Chen Weigen, Li Gang. The influence of harmonic wave on grid power metering device . Power system technology, 2010, 36(3): 23-27.

Google Scholar

[12] Li Junhua, Li Ming, Yuan Lihua. The pseudo parallel genetic algorithm based on clustering . Pattern recognition and artificial intelligence, 2011, 22(2): 188-194.

Google Scholar