Redundantly full-actuated parallel manipulator takes number of actuations exceeding its degree of freedom, and actuation coordination makes its basis of stable operation. This paper studies the coordination dynamics of general redundantly full-actuated parallel manipulator and derives coordination dynamics models for driving force coordination and internal force regulation respectively. Associated with coordination dynamics models, two neural network synchronous control methods are proposed for each situation correspondingly. Self-learning synchronous algorithms for those methods are designed additionally. Manipulator 6PUS+UPU is taken as a prototype for co-simulations and experiments. Results reveal that the two methods proposed above could improve actuation coordination and internal force precision of redundantly full-actuated parallel manipulator respectively. This paper provides new dynamics-based control methods for the research and control application of parallel manipulator with redundant actuation.

冗余全驱动并联机械手所进行的致动次数超过其自由度，并且致动协调成为其稳定运行的基础。研究了通用冗余全驱动并联机械手的协调动力学，推导了分别用于驱动力协调和内力调节的协调动力学模型。结合协调动力学模型，针对每种情况分别提出了两种神经网络同步控制方法。这些方法的自学习同步算法是额外设计的。机械手6 PUS + UPU被用作协同仿真和实验的原型。结果表明，以上两种方法分别可以提高冗余度全驱动并联机械手的驱动协调性和内力精度。本文为冗余驱动并联机器人的研究和控制应用提供了一种基于动力学的控制方法。