Mulero-Martínez Juan Ignacio
Departamento de Ingeniería de Sistemas y Automática, Universidad Politécnica de Cartagena, Cartagena 30203, Spain.
IEEE Trans Neural Netw. 2007 May;18(3):865-79. doi: 10.1109/TNN.2007.894070.
In this paper, a dynamic neurocontroller for positioning of robots based on static and parametric neural networks (NNs) has been developed. This controller is based on Christoffel symbols of first kind in order to carry out coriolis/centripetal matrix. Structural properties of robots and Kronecker product has been taken into account to develop NNs to approximate nonlinearities. The weight updating laws have been obtained from a nonlinear strategy based on Lyapunov energy that guarantees both stability and boundedness of signals and weights. The NN weights are tuned online with no "offline learning phase" and are initialized to zero. The neurocontroller improves the implementation with respect to other dynamic NNs used in the literature.
本文开发了一种基于静态和参数神经网络（NN）的机器人定位动态神经控制器。该控制器基于第一类克里斯托费尔符号来实现科里奥利/向心力矩阵。在开发神经网络以逼近非线性时，考虑了机器人的结构特性和克罗内克积。权重更新律是从基于李雅普诺夫能量的非线性策略获得的，该策略保证了信号和权重的稳定性和有界性。神经网络权重无需“离线学习阶段”即可在线调整，且初始化为零。与文献中使用的其他动态神经网络相比，该神经控制器改进了实现方式。
