Lewis F L, Liu K, Yesildirek A
Automation and Robotics Res. Inst., Texas Univ., Arlington, TX.
IEEE Trans Neural Netw. 1995;6(3):703-15. doi: 10.1109/72.377975.
A neural net (NN) controller for a general serial-link robot arm is developed. The NN has two layers so that linearity in the parameters holds, but the "net functional reconstruction error" and robot disturbance input are taken as nonzero. The structure of the NN controller is derived using a filtered error/passivity approach, leading to new NN passivity properties. Online weight tuning algorithms including a correction term to backpropagation, plus an added robustifying signal, guarantee tracking as well as bounded NN weights. The NN controller structure has an outer tracking loop so that the NN weights are conveniently initialized at zero, with learning occurring online in real-time. It is shown that standard backpropagation, when used for real-time closed-loop control, can yield unbounded NN weights if (1) the net cannot exactly reconstruct a certain required control function or (2) there are bounded unknown disturbances in the robot dynamics. The role of persistency of excitation is explored.
开发了一种用于通用串联连杆机器人手臂的神经网络（NN）控制器。该神经网络有两层，因此参数具有线性，但“网络功能重构误差”和机器人干扰输入被视为非零。使用滤波误差/无源性方法推导了神经网络控制器的结构，从而得出了新的神经网络无源性特性。在线权重调整算法包括对反向传播的校正项以及添加的增强信号，可保证跟踪以及有界的神经网络权重。神经网络控制器结构有一个外部跟踪回路，因此神经网络权重可以方便地初始化为零，并在实时在线学习。结果表明，当用于实时闭环控制时，如果（1）网络不能精确重构某个所需的控制函数，或者（2）机器人动力学中存在有界未知干扰，标准反向传播可能会产生无界的神经网络权重。探讨了持续激励的作用。
