西安科技大学学报2017，Vol.37Issue(6)：927-931,5.DOI:10.13800/j.cnki.xakjdxxb.2017.0624
粒子群算法优化BP神经网络的变载荷自平衡控制系统
Variable load self-balancing control system based on PSO-optimized BP neural network
倪守斌 1程武山1
作者信息
- 1. 上海工程技术大学机械工程学院,上海201620
- 折叠
摘要
Abstract
In order to solve the difficulty in conventional PID control with time-varying load balance system in real-time and precise adjustment of the load changes,on the base of BP neural network and using particle swarm algorithm (PSO)optimized BP neural network,the convergence speed of neural network was improved,and the algorithm was applied to the two-wheel balance vehicle control system.A dynamic model of two-wheel balance vehicle was established.We introduce the structure,principle and experimental method of the system,build the experimental platform for the two-wheel balance vehicle on the test case of mutation load,get the output angle of tilt body posture sensor using two-wheel balance vehicle experimental platform on the vehicle body,contrast output tilt angle changes before and after applied load and neural network optimization.The test results shows that the particle swarm algorithm (PSO)-optimized BP neural network technology meets the varying load in the two wheel self-balancing vehicle control,the dynamic self-balancing of the self-balancing vehicle is realized,and the anti-interference capability is improved.The advantages of the optimized algorithm in self-balancing,anti external interference and shortening the adjustment time are verified.关键词
时变负载/自平衡/粒子群算法/PID神经网络Key words
time-varying load/self-balancing/particle swarm optimization/PID neural network分类
信息技术与安全科学引用本文复制引用
倪守斌,程武山..粒子群算法优化BP神经网络的变载荷自平衡控制系统[J].西安科技大学学报,2017,37(6):927-931,5.
