Design of BP neural network controller for ball-beam system
Design of BP neural network controller for ball-beam system
复制标题
球梁系统BP神经网络控制器设计
DOI：
10.1109/imcec.2016.7867379
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Yongxin Liu
中科院分区：
文献类型：
--
作者：
Liqing Gao;Yongxin Liu
针对球杆系统设计了BP神经网络稳定控制器。该控制器由三层前向神经网络构成，网络权值采用Fletcher-Reeves共轭梯度算法进行调整。控制器的神经网络训练样本由根轨迹控制器获得。控制器经过离线训练后，对球杆系统进行在线控制。通过离线仿真和在真实的系统中的应用，结果表明，该神经网络控制器响应速度快，超调量小，在球杆系统的控制中是可行的。它比根轨迹控制器更有效。
The BP NN (back propagation neural network) stable controller for ball-beam system is designed in this paper. The controller is constructed by a three-layer forward neural network, and Fletcher-Reeves conjugate gradient algorithm is used to adjust net weights. The NN training samples for controller is obtained from root locus controller. After off-line trained, controller is put into on-line control for ball-beam system. Through simulation off line and used in real system, the result shows that this NN controller responses fast, and overshoot is smaller and feasible in the control of ball-beam system. It is more effective than the root locus controller.