Application of Optimized BPNN Based on QGA in PID Control of Coal Mine Detection Robots

Yang Yang He; Zhi Gang Niu

doi:10.4028/www.scientific.net/AMR.605-607.1605

Paper Titles

Robust Adaptive Sliding Mode Synchronous Control for a Planar Redundantly Actuated Parallel Manipulator
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Research on Pneumatic Control System of Box Unfolding and Closing Mechanism of Horizontal Type Boxing Line
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Path Planning with Motion Optimization for Car Body-in-White Industrial Robot Applications
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Industrial Robot Control Using Inertial Navigation System
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Application of Optimized BPNN Based on QGA in PID Control of Coal Mine Detection Robots
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An Introduction to Parallel Robot Mechanism
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Ant Colony Optimization Based on Local Optima Breaking Mechanism for Unmanned Vehicle Path Planning in Cross-Country Environment
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Study on Genetic Fuzzy Wavelet Neural Network Controller of Robotic Manipulator
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A New Design of Programmable Multi-Axis Controller
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 605-607Application of Optimized BPNN Based on QGA in PID...

Application of Optimized BPNN Based on QGA in PID Control of Coal Mine Detection Robots

Abstract:

This thesis regards TUT-CMDR type coal mine detection robots as the research object and put forward an application of optimized BP neural network based on Quantum Genetic Algorithm in PID Control of motor speed. Transfer function model of speed control system of TUT-CMDR motor was established. Firstly, initial weights and thresholds of BP neural network were optimized by Quantum Genetic Algorithm, and then BP neural network was designed to adjust the parameters of PID on line. Finally, the results show that the algorithm is feasible and superiority.

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Periodical:

Advanced Materials Research (Volumes 605-607)

Pages:

1605-1608

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.605-607.1605

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Online since:

December 2012

Authors:

Yang Yang He, Zhi Gang Niu

Keywords:

BP Neural Network (BPNN), MATLAB/SIMULINK, PID Control, Quantum Genetic Algorithm

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References

[1] Z. S. Jiang, C. Song, F. X. Zhu and W. C. Si: Armament Automation, Vol.28 (2004) No.3, pp.52-55. (In Chinese)

Google Scholar

[2] MT-FR Users manual (Shanghai Ingenious Automation Technology Co. Ltd, China 2010). (In Chinese)

Google Scholar

[3] S.P. Chen, R. Dong, X. L. Luo: MATLAB 30 cases analysis of neural network (Peking University Press, China 2010). (In Chinese)

Google Scholar

[4] P. Zhang, S. P. Gao and L. D. Wang: Computer Engineering, Vol.37 (2011), No.23, pp.124-126. (In Chinese)

Google Scholar

[5] L. J. Kun: The advanced PID control and Simulation of MATLAB (Publishing House of Electronics Industry, China 2004). (In Chinese)

Google Scholar

[6] Z. G. Liu, M. S. Tong, M. Y. Wang and P. Huang: Computer Simulation, Vol.26 (2009) No.10, pp.207-211. (In Chinese)

Google Scholar

[7] Y.L. Xiong: Fundamentals of Robot Technique (Huazhong University of Science and Technology Press, China 1996). (In Chinese)

Google Scholar

[8] Z.G. Niu and X. Wang: 2nd International Conference on Frontiers of Manufacturing and Design Science (Taichung, Taiwan December 11-13, 2011), Vol.121-126 (2012), pp.3356-3360.

Google Scholar

[9] J. X. Wang, Q. S. Lin and Y. F. Yao, Y. B. Liu: Industry and Mine Automation, Vol.1671-251X (2010) No.5, pp.68-70. (In Chinese)

Google Scholar