三维切削力传感器是智能数控车床的重要组成部分,通过切削力传感器可以间接分析出加工出现的各种问题,如工件加工质量和刀具磨损情况等。设计了一款低交叉干扰的全对中一体化车削力传感器,通过析因分析筛选设计变量,采取最佳空间填充技...三维切削力传感器是智能数控车床的重要组成部分,通过切削力传感器可以间接分析出加工出现的各种问题,如工件加工质量和刀具磨损情况等。设计了一款低交叉干扰的全对中一体化车削力传感器,通过析因分析筛选设计变量,采取最佳空间填充技术和有限元分析结合的方法生成实验设计模型,根据实验设计模型开发了灰狼算法优化的反向传播神经网络的高精度非线性代理模型,对比分析三种优化算法的Pareto前沿,选择TOP算法对代理模型进行多目标优化。优化后:传感器固有频率为1.561 k Hz,满足机床主轴转速在23415 r/min下使用,传感器的平均变形量提升了一倍,根据惠斯通电桥输出电压计算可得,传感器各方向灵敏度提升了10倍左右,Fc方向交叉干扰消除,整体交叉干扰最高为1.9%。展开更多
Reasons and realities such as being non-linear of dynamical equations,being lightweight and unstable nature of quadrotor,along with internal and external disturbances and parametric uncertainties,have caused that the ...Reasons and realities such as being non-linear of dynamical equations,being lightweight and unstable nature of quadrotor,along with internal and external disturbances and parametric uncertainties,have caused that the controller design for these quadrotors is considered the challenging issue of the day.In this work,an adaptive sliding mode controller based on neural network is proposed to control the altitude of a quadrotor.The error and error derivative of the altitude of a quadrotor are the inputs of neural network and altitude sliding surface variable is its output.Neural network estimates the sliding surface variable adaptively according to the conditions of quadrotor and sets the altitude of a quadrotor equal to the desired value.The proposed controller stability has been proven by Lyapunov theory and it is shown that all system states reach to sliding surface and are remaining in it.The superiority of the proposed control method has been proven by comparison and simulation results.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
文摘三维切削力传感器是智能数控车床的重要组成部分,通过切削力传感器可以间接分析出加工出现的各种问题,如工件加工质量和刀具磨损情况等。设计了一款低交叉干扰的全对中一体化车削力传感器,通过析因分析筛选设计变量,采取最佳空间填充技术和有限元分析结合的方法生成实验设计模型,根据实验设计模型开发了灰狼算法优化的反向传播神经网络的高精度非线性代理模型,对比分析三种优化算法的Pareto前沿,选择TOP算法对代理模型进行多目标优化。优化后:传感器固有频率为1.561 k Hz,满足机床主轴转速在23415 r/min下使用,传感器的平均变形量提升了一倍,根据惠斯通电桥输出电压计算可得,传感器各方向灵敏度提升了10倍左右,Fc方向交叉干扰消除,整体交叉干扰最高为1.9%。
基金authorities of East Tehran Branch,Islamic Azad University,Tehran,Iran,for providing support and necessary facilities
文摘Reasons and realities such as being non-linear of dynamical equations,being lightweight and unstable nature of quadrotor,along with internal and external disturbances and parametric uncertainties,have caused that the controller design for these quadrotors is considered the challenging issue of the day.In this work,an adaptive sliding mode controller based on neural network is proposed to control the altitude of a quadrotor.The error and error derivative of the altitude of a quadrotor are the inputs of neural network and altitude sliding surface variable is its output.Neural network estimates the sliding surface variable adaptively according to the conditions of quadrotor and sets the altitude of a quadrotor equal to the desired value.The proposed controller stability has been proven by Lyapunov theory and it is shown that all system states reach to sliding surface and are remaining in it.The superiority of the proposed control method has been proven by comparison and simulation results.
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.
