基于PSO优化神经网络的滚珠丝杠副进给系统自适应滑模控制
daptive sliding mode control of ball screw drives based on PSO neural network
为了提高滚珠丝杠副进给系统的跟踪精度，针对被控对象具有摩擦和未知干扰的特征，采用了基于PSO优化神经网络的自适应滑模控制。因为神经网络的隐含层中心和宽度对网络效果影响不可忽略，所以基于粒子群进行改进，并通过仿真对比，证明改进后收敛速度更快，误差更小。根据滚珠丝杠副的刚体模型，选择未知项利用神经网络进行逼近，并设计具有稳定性的滑模自适应律。实验结果表明：本文设计的控制器跟踪效果较好，相比于PID和优化前方法，可把最大跟踪误差分别从55.05μm和26.53μm缩小至16.85μm。?????
In order to improve the tracking performance of ball screw drives, because controlled project is affected by the friction and some unknown disturbs, adaptive sliding mode control based on PSO neural network is used. For the non-negligible effect of initial start center and width on neural network, this paper use particle swarm optimization. By contrasting simulation results, the fast rate of convergence and small error are proved. According to the rigid model of ball screw drives, using neutral network to approach the unknown terms. Designing a stable sliding mode controller. The experiment result shows that the tracking performance is good. Compared with PID controller and un-optimized method, the tracking error is respectively reduced from 55.05μm and 26.53μm to 16.85μm. ?????
刘碧茜、汤文成
自动化技术、自动化技术设备机械零件、传动装置机械学
机械制造自动化滚珠丝杠副神经网络粒子群算法自适应滑模控制
machinery manufacture automationball screwneutral networkparticle swarm optimizationadaptive sliding mode control ?????
刘碧茜,汤文成.基于PSO优化神经网络的滚珠丝杠副进给系统自适应滑模控制[EB/OL].(2018-03-30)[2025-08-18].http://www.paper.edu.cn/releasepaper/content/201803-277.点此复制
评论