Loreto G, Garrido R
IEEE Trans Neural Netw. 2006 Jul;17(4):953-965. doi: 10.1109/TNN.2006.875993.
In this paper, we propose a stable neurovisual servoing algorithm for set-point control of planar robot manipulators in a fixed-camera configuration an show that all the closed-loop signals are uniformly ultimately bounded (UUB) and converge exponentially to a small compact set. We assume that the gravity term and Jacobian matrix are unknown. Radial basis function neural networks (RBFNNs) with online real-time learning are proposed for compensating both gravitational forces and errors in the robot Jacobian matrix. The learning rule for updating the neural network weights, similar to a back propagation algorithm, is obtained from a Lyapunov stability analysis. Experimental results on a two degrees of freedom manipulator are presented to evaluate the proposed controller.
在本文中，我们提出了一种稳定的神经视觉伺服算法，用于在固定摄像机配置下对平面机器人操纵器进行设定点控制，并表明所有闭环信号都是一致最终有界（UUB）的，并且指数收敛到一个小的紧致集。我们假设重力项和雅可比矩阵是未知的。提出了具有在线实时学习能力的径向基函数神经网络（RBFNN），用于补偿重力和机器人雅可比矩阵中的误差。类似于反向传播算法的用于更新神经网络权重的学习规则，是从李雅普诺夫稳定性分析中得到的。给出了在两自由度操纵器上的实验结果，以评估所提出的控制器。
