Cable-driven parallel robots have been studied by many researchers in the past decades. The Jacobian of a cable-driven parallel robot may not be determined in some applications such as rehabilitation. In order to control the pose of a fully constrained cable-driven parallel robot with unknown Jacobian and driven by torque-controlled actuators, a learning-based control framework consisting of a robust controller and a neural network in series is proposed in this article. The neural network takes over the role of the Jacobian by mapping a wrench applied on the end-effector of the cable-driven parallel robot at a pose in the task space to a set of cable tensions in the joint space. In this way, the cable-driven parallel robot can be controlled by cable tensions derived from such a mapping, rather than solving the inverse dynamics problem based on the Jacobian. As an example, a control strategy is developed to demonstrate how the proposed control framework works. The control strategy includes a proportional–integral–derivative controller and a feedforward neural network. Simulation results show that the control strategy can successfully control a cable-driven parallel robot with four cables, three degrees of freedom, and unknown Jacobian.

中文翻译：

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基于学习的未知雅可比电缆驱动并联机器人控制框架

在过去的几十年中，许多研究人员对电缆驱动的并联机器人进行了研究。在某些应用（例如康复）中，可能无法确定电缆驱动的并联机器人的雅可比矩阵。为了控制具有未知雅可比行列式并由扭矩控制执行器驱动的完全约束电缆驱动并联机器人的姿态，本文提出了一种由鲁棒控制器和神经网络串联组成的基于学习的控制框架。神经网络通过将应用在绳驱动并联机器人末端执行器上的扳手在任务空间中的某个姿势映射到关节空间中的一组绳索张力来接管雅可比矩阵的作用。通过这种方式，缆索驱动的并联机器人可以通过从这种映射导出的缆索张力来控制，而不是基于雅可比求解逆动力学问题。作为一个例子，开发了一个控制策略来演示所提出的控制框架是如何工作的。控制策略包括比​​例-积分-微分控制器和前馈神经网络。仿真结果表明，该控制策略能够成功控制具有四根缆索、三个自由度、未知雅可比矩阵的缆索驱动并联机器人。