Analytic Deep Neural Network-Based Robot Control
Analytic Deep Neural Network-Based Robot Control
复制标题
基于分析深度神经网络的机器人控制
DOI：
10.1109/tmech.2022.3175903
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
C. Cheah
中科院分区：
文献类型：
--
作者：
Huu;C. Cheah
神经网络以其强大的非线性函数逼近能力，在机器人控制中得到了广泛的应用。然而，现有的关于机器人反馈控制的文献主要集中在浅层网络上，这些网络的分析只针对输出权重，并且通常要求参数的线性。这是由于深层网络的收敛分析比较困难。由于稳定性和收敛在机器人控制中至关重要，我们的主要目标是开发一个理论框架，以便以安全和可预测的方式在机器人学中使用深度网络。在本文中，我们使用深度网络来逼近运动学未知的机器人的雅可比矩阵。提出了一种分析型分层深度学习框架，该框架通过逐步构建和训练深度网络，并保证在线学习过程中跟踪误差的收敛。给出了在工业机器人上执行跟踪控制任务的实验结果，验证了该方法的有效性。
Neural networks have been extensively used in robot control for various applications because of their powerful capability in approximation of nonlinear functions. However, existing literature on feedback control of robots mainly focuses on shallow networks where the analysis is developed for the output weights only and the linearity in parameters is often a requirement. This is due to the fact that convergence analysis is difficult for deep networks. Since stability and convergence are critical in robot control, our main aim is to develop a theoretical framework for using deep networks in robotics in a safe and predictable manner. In this article, we use a deep network to approximate the Jacobian matrix of a robot with unknown kinematics. An analytic layer-wise deep learning framework is proposed where the deep network is progressively built and trained, and the convergence of the tracking error is guaranteed during the online learning process. The experimental results for tracking control tasks performed on an industrial robot are given to illustrate the effectiveness of the proposed method.