摘要
一个靠近链的机器人比一个开链的机器人有几个优点，例如高机械的刚硬，高收费载重，高精确。一个机器人的精确轨道控制在实际使用是必要的。这篇论文基于光线的基础功能(RBF ) 论述一个适应比例的不可分的 differential (PID ) 控制算法为轨道追踪 two-degree-of-freedom (2-DOF ) 的神经网络靠近链的机器人。在这个计划，一个 RBF 神经网络被用来接近机器人的未知非线性的动力学同时， PID 参数能在网上被调整，高精确能被获得。模拟结果证明控制算法精确地追踪 2-DOF 靠近链的机器人轨道。结果也显示系统坚韧性和追踪的性能比经典 PID 方法优异。
A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper presents an adaptive proportional integral differential （PID） control algorithm based on radial basis function （RBF） neural network for trajectory tracking of a two-degree-of-freedom （2-DOF） closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.
基金
Project supported bY the National Natural Science Foundation of China （Grant No.50375085）, and the Natural Science Foundation of Shandong Province （Grant No.Y2002F13）
关键词
自由度闭链机器人
神经网络
自适应控制
自动控制
closed-chain robot, radial basis function （RBF） neural network, adaptive proportional integral differential （PID） control, identification, neural network
