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Proceedings Paper

Algorithm and achieve of self-balancing two-wheeled control system based on PID neural network
Author(s): Yan Zhao; Jiang Hua Wang; Qi Wang
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Paper Abstract

For self-balancing two-wheeled vehicle position and angle control implementation issues, propose a control algorithm of self-balanced two-wheeled vehicle based on PID neural network. The algorithm overcomes the problem of PID controller generalization is weak, and can avoid over fitting and the BP neural network controller into a local optimum. The algorithm of the controller is constituted by a three-tier network, the first floor is the input layer, which receives two-wheel self-balancing vehicle position and Angle feedback information; a second layer of PID neuron layer, which is connected to the input layer, the state transition function respectively proportional function, integral function and the differential function; the third layer is the output layer, by weight in various proportions hidden layer output, the final composition of the control output of the controller. Control system fusion using kalman filter algorithm to get the self-balanced vehicle's attitude information. Through self-balancing two-wheeled vehicle actual experimental platform to verify the effectiveness of the control algorithm implementation.

Paper Details

Date Published: 3 December 2015
PDF: 4 pages
Proc. SPIE 9794, Sixth International Conference on Electronics and Information Engineering, 97942Y (3 December 2015); doi: 10.1117/12.2212909
Show Author Affiliations
Yan Zhao, Yanching Institute of Technology (China)
Jiang Hua Wang, North China Institute of Science and Technology (China)
Qi Wang, Yanching Institute of Technology (China)

Published in SPIE Proceedings Vol. 9794:
Sixth International Conference on Electronics and Information Engineering
Qiang Zhang, Editor(s)

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