Nonlinear tracking control enabling a dynamical system to track a desired
trajectory is fundamental to robotics, serving a wide range of civil and
defense applications. In control engineering, designing tracking control
requires complete knowledge of the system model and equations. We develop a
model-free, machine-learning framework to control a two-arm robotic manipulator
using only partially observed states, where the controller is realized by
reservoir computing. Stochastic input is exploited for training, which consists
of the observed partial state vector as the first and its immediate future as
the second component so that the neural machine regards the latter as the
future state of the former. In the testing (deployment) phase, the
immediate-future component is replaced by the desired observational vector from
the reference trajectory. We demonstrate the effectiveness of the control
framework using a variety of periodic and chaotic signals, and establish its
robustness against measurement noise, disturbances, and uncertainties.

通过模型无关的机器学习框架和储水计算方法，实现对双臂机器人操纵器的跟踪控制，仅使用部分观测状态，并且该控制框架在周期性和混沌信号方面表现出有效性和对测量噪声、干扰和不确定性的鲁棒性。