摘要
为了克服机械摩擦、外界干扰和模型误差等不确定性对工业机器人双臂运动轨迹控制精度的影响,设计了一种基于自适应神经网络的工业机器人双臂协同鲁棒控制方法。首先,建立了带有各类不确定性的工业机器人双臂动力学模型;然后,通过构造障碍Lyapunov函数设计了带有不确定性的协同控制律,并设计了自适应神经网络对系统中的不确定性进行估计,从而得到工业机器人双臂协同鲁棒控制律;最后,利用Lyapunov稳定性理论证明了设计的协同鲁棒控制律能够将工业机器人双臂的轨迹跟踪误差、速度跟踪误差和不确定性估计误差约束在一个任意小的邻域内。仿真结果表明,设计的自适应神经网络可准确估计出工业机器人双臂系统中的不确定性,最大估计误差仅为0.04 N·m,提出的协同鲁棒控制律能够稳定、准确地跟踪轨迹控制指令,最大轨迹跟踪误差仅为1.3 mm,从而验证了设计方法的合理性。在三维空间固定坐标定位测试中,提出的协同鲁棒控制律与其他几种方法相比具有更高的控制精度,平均定位误差和最大定位误差分别仅为1.1 mm和1.4 mm,表现出了更强的鲁棒性和更优的工程适用性。
To overcome the influence of uncertainties such as mechanical friction,external interference,and model errors on the accuracy of industry robot dual-arm motion trajectory control,a collaborative robust control method for industry robot dual-arm based on adaptive neural network was designed.Firstly,a dynamic model of industry robot dual-arm with various uncertainties was established.Then,a collaborative control law with uncertainty was designed by constructing an obstacle Lyapunov function,and an adaptive neural network was designed to estimate the uncertainty of the system,obtaining a robust collaborative control law for in-dustry robot dual-arm.Finally,the Lyapunov stability theory was used to demonstrate that the designed collaborative robust control law can constrain the trajectory tracking error,velocity tracking error,and uncertainty estimation error of the industry robot dual-arm within an arbitrarily small neighborhood.The simulation results show that the designed adaptive neural network can accurately estimate the uncertainty in the industry robot dual-arm system,and the maximum estimation error is only 0.04 N·m.The proposed collaborative robust control law can stably and accurately track trajectory control instructions,and the maximum trajectory tracking error is only 1.3 mm,verifying the rationality of the designed method.In the fixed coordinate positioning test in 3D space,the proposed collaborative robust control law has higher control accuracy compared with other methods,the average and maximum positioning error are only 1.1 mm and 1.4 mm,respectively,demonstrating stronger robustness and better engineering applicability.
作者
贾英霞
王东辉
JIA Yingxia;WANG Donghui(Shangqiu Vocational Education Center,Shangqiu 476000,China;Research Center of Henan CNC Engineering Technology,Zhengzhou 450001,China)
出处
《现代制造工程》
CSCD
北大核心
2024年第6期61-68,共8页
Modern Manufacturing Engineering
基金
河南省高等学校重点科研项目(24B520030)
河南省职业教育教学改革研究与实践项目(202302838)。
