针对高速龙门式包带机的驱动过程平行轴产生噪声、振动和卡滞等问题,造成其驱动过程不同步的影响,提出一种降阶扩张状态观测器(reduced-order extended state observer,RESO)的线性自抗扰控制(linear active disturbance rejection cont...针对高速龙门式包带机的驱动过程平行轴产生噪声、振动和卡滞等问题,造成其驱动过程不同步的影响,提出一种降阶扩张状态观测器(reduced-order extended state observer,RESO)的线性自抗扰控制(linear active disturbance rejection contro,LADRC)控制器和TS型模糊神经网络(TS fuzzy neural network,TS-FNN)同步补偿器相结合的控制方法。首先,针对高速龙门式包带机单轴的噪声、摩擦力和振动对伺服系统控制精度的影响,采用RESO的LADRC算法,以抑制控制系统的外部扰动和减少参数调节数量,从而提高位置跟踪精度;同时,针对平行轴中双直线电机因参数摄动和机械耦合等不确定扰动对位置同步精度的影响,采用交叉耦合的控制方法并结合TS-FNN同步补偿器来提高两平行轴的同步精度。通过实验对比验证,所采用的控制策略能有效减少高速龙门式包带机的单轴的跟踪误差,并提高平行轴的同步误差和抗扰性。展开更多
Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a nov...Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a novel method via T-S cloud inference network optimized by genetic algorithm(GA) is proposed. T-S cloud inference network is constructed with T-S fuzzy neural network and the cloud model. So, the rapid of fuzzy logic and the uncertainty of cloud model for processing data are both taken into account. What's more, GA possesses good parallel design structure and global optimization characteristics. Compared with the simulation recognition results of traditional BP Algorithm, GA is more accurate and effective. Moreover, virtual reality technology is introduced into the field of shape control by Lab VIEW, MATLAB mixed programming. And virtual flatness pattern recognition interface is designed.Therefore, the data of engineering analysis and the actual model are combined with each other, and the shape defects could be seen more lively and intuitively.展开更多
基金Project(LJRC013)supported by the University Innovation Team of Hebei Province Leading Talent Cultivation,China
文摘Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a novel method via T-S cloud inference network optimized by genetic algorithm(GA) is proposed. T-S cloud inference network is constructed with T-S fuzzy neural network and the cloud model. So, the rapid of fuzzy logic and the uncertainty of cloud model for processing data are both taken into account. What's more, GA possesses good parallel design structure and global optimization characteristics. Compared with the simulation recognition results of traditional BP Algorithm, GA is more accurate and effective. Moreover, virtual reality technology is introduced into the field of shape control by Lab VIEW, MATLAB mixed programming. And virtual flatness pattern recognition interface is designed.Therefore, the data of engineering analysis and the actual model are combined with each other, and the shape defects could be seen more lively and intuitively.
