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基于径向基函数神经网络控制的机械臂轨迹误差研究
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广东省科技计划项目(2014A010104016)


Research on Trajectory Error of Mechanical Arm Based on Radial Basis Function Neural Network Control
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    摘要:

    当前,机械臂关节运动轨迹容易受到外界环境的干扰,导致运动轨迹不稳定,抖动现象特别严重,不能很好地满足轨迹跟踪任务的要求。对此,文中创建机械臂双关节运动简图模型,采用径向基函数(RBF)神经网络自适应控制方法跟踪机械臂关节的运动轨迹。分析了机械臂运动轨迹所产生的误差,设计了机械臂关节神经网络自适应控制器,引用李雅普诺夫函数对控制器的稳定性和收敛性进行了证明。结合具体实例,借助于Matlab软件对机械臂双关节的运动轨迹追踪误差进行仿真。同时,与模糊PID控制的仿真误差进行对比和分析。仿真曲线显示,机械臂关节采用RBF神经网络自适应控制方法,运动轨迹追踪所产生的误差较小,输入力矩的振动幅度相对较小。因此,机械臂关节末端采用RBF神经网络自适应控制器,可以降低运动轨迹的跟踪误差,改善振动现象。

    Abstract:

    At present,the trajectory of mechanical arm joint is vulnerable to the interference of the external environment, which leads to unstable trajectories and serious dithering phenomenon,and it cant satisfy the requirement of trajectory tracking task well.so the movement diagram model of mechanical arm double joint was created by using radial basis function (RBF) neural network adaptive control method to track the trajectory of mechanical arm joint.The error generated by mechanical arm trajectory was analyzed by designing the mechanical arm joint adaptive neural network controller,and lyapunov function was used to prove the stability and convergence of controller.Combined with specific examples,the trajectory tracking error of the double joint of the mechanical arm was simulated by Matlab software.At the same time, the simulation error of fuzzy PID control was compared and analyzed.The simulation curves show that the error generated by trajectory tracking is smaller and the vibration amplitude of input torque is relatively small by using RBF neural network adaptive control method for mechanical arm joints. Therefore, Mechanical arm joint end by using RBF neural network adaptive controller can reduce the tracking error of trajectory and improve the vibration phenomenon.

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刘益标,陈均.基于径向基函数神经网络控制的机械臂轨迹误差研究[J].机床与液压,2018,46(15):105-108.
. Research on Trajectory Error of Mechanical Arm Based on Radial Basis Function Neural Network Control[J]. Machine Tool & Hydraulics,2018,46(15):105-108

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  • 在线发布日期: 2019-07-04
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