Unlike most control systems, kinematic uncertainty is present in robot control systems in addition to dynamic uncertainty. The use of different types of external sensors in various configurations also results in different sensory transformation or Jacobian matrices and thus leads to different kinematic models. Currently, there is no systematic theoretical framework in developing data-driven neural network (NN) learning and control methods for task-space tracking control of robots with unknown kinematics and dynamics. The existing NN controllers are limited to either dynamic control or kinematic control without considering the interaction between the inner control loop and the outer control loop. In this paper, a NN based data driven offline learning algorithm and an online learning controller are proposed, which are combined in a complementary way. The proposed task-space control algorithms can be implemented on robotic systems with closed control architecture by considering the interaction with the inner control loop. Theoretical analyses are presented to show the stability of the systems and experimental results are presented to illustrate the performance of the proposed learning algorithms.

与大多数控制系统不同，除了动态不确定性之外，机器人控制系统还存在运动不确定性。在各种配置中使用不同类型的外部传感器也会导致不同的感觉转换或雅可比矩阵，从而导致不同的运动学模型。当前，在开发运动学和动力学未知的机器人的任务空间跟踪控制的数据驱动神经网络（NN）学习和控制方法时，没有系统的理论框架。现有的NN控制器仅限于动态控制或运动学控制，而无需考虑内部控制回路和外部控制回路之间的相互作用。本文提出了一种基于神经网络的数据驱动离线学习算法和在线学习控制器，两者以互补的方式结合在一起。通过考虑与内部控制回路的交互作用，可以在具有封闭控制架构的机器人系统上实现所提出的任务空间控制算法。进行理论分析以表明系统的稳定性，并给出实验结果以说明所提出的学习算法的性能。