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.

翻译：非线性跟踪控制使动力系统能够跟踪期望轨迹，是机器人技术的基础，服务于广泛的民用和国防应用。在控制工程中，设计跟踪控制需要完全了解系统模型和方程。我们开发了一种无模型的机器学习框架，仅利用部分观测状态来控制双臂机器人操纵器，其中控制器通过储层计算实现。利用随机输入进行训练，训练数据由两部分组成：第一部分为观测到的部分状态向量，第二部分为其紧邻未来状态，使得神经机器将后者视为前者的未来状态。在测试（部署）阶段，紧邻未来状态分量被参考轨迹中的期望观测向量替代。我们通过多种周期性和混沌信号验证了该控制框架的有效性，并证明了其对测量噪声、干扰和不确定性的鲁棒性。