IEEE Trans Cybern. 2015 Mar;45(3):521-32. doi: 10.1109/TCYB.2014.2329931. Epub 2014 Jun 24.
To achieve an excellent dual-arm coordination of the humanoid robot, it is essential to deal with the nonlinearities existing in the system dynamics. The literatures so far on the humanoid robot control have a common assumption that the problem of output hysteresis could be ignored. However, in the practical applications, the output hysteresis is widely spread; and its existing limits the motion/force performances of the robotic system. In this paper, an adaptive neural control scheme, which takes the unknown output hysteresis and computational efficiency into account, is presented and investigated. In the controller design, the prior knowledge of system dynamics is assumed to be unknown. The motion error is guaranteed to converge to a small neighborhood of the origin by Lyapunov's stability theory. Simultaneously, the internal force is kept bounded and its error can be made arbitrarily small.
为了实现人形机器人的卓越双臂协调，处理系统动力学中存在的非线性问题至关重要。迄今为止，有关人形机器人控制的文献普遍假设可以忽略输出滞后问题。然而，在实际应用中，输出滞后现象广泛存在，限制了机器人系统的运动/力性能。本文提出并研究了一种自适应神经控制方案，该方案考虑了未知的输出滞后和计算效率。在控制器设计中，假设系统动力学的先验知识未知。通过 Lyapunov 稳定性理论，保证运动误差收敛到原点的小邻域内。同时，保持内力有界，并使误差任意小。
