Remote center of motion (RCM) constraint has attracted many research interests as one of the key challenges for robot-assisted minimally invasive surgery (RAMIS). Although it has been addressed by many studies, few of them treated the motion constraint with an independent workspace solution, which means they rely on the kinematics of the robot manipulator. This makes it difficult to replicate the solutions on other manipulators, which limits their population. In this paper, we propose a novel control framework by incorporating model predictive control (MPC) with the fuzzy approximation to improve the accuracy under the motion constraint. The fuzzy approximation is introduced to manage the kinematic uncertainties existing in the MPC control. Finally, simulations were performed and analyzed to validate the proposed algorithm. By comparison, the results prove that the proposed algorithm achieved success and satisfying performance in the presence of external disturbances.

作为机器人辅助微创手术 (RAMIS) 的主要挑战之一，远程运动中心 (RCM) 约束吸引了许多研究兴趣。尽管许多研究已经解决了这个问题，但很少有人使用独立的工作空间解决方案来处理运动约束，这意味着它们依赖于机器人机械手的运动学。这使得很难在其他操纵器上复制解决方案，从而限制了它们的数量。在本文中，我们通过将模型预测控制 (MPC) 与模糊近似相结合，提出了一种新颖的控制框架，以提高运动约束下的精度。引入模糊近似来管理 MPC 控制中存在的运动学不确定性。最后，通过仿真和分析来验证所提出的算法。通过对比，