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Nonlinear model predictive control (NMPC) based trajectory tracking on EAST Articulated Maintenance Arm (EAMA)
Fusion Engineering and Design ( IF 1.9 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.fusengdes.2020.112102
Xuanchen Zhang , Haifeng Yao , Qiong Zhang , Zhiwei Hao , Hongtao Pan , Yang Yang , Yong Cheng , Yuntao Song

Abstract EAST Articulated Maintenance Arm (EAMA) is a 8-DOF redundant articulated serial manipulator utilized to conduct maintenance tasks in EAST (Experimental Advanced Superconducting Tokamak). Due to the redundancy and total length (8.7395 m) of manipulator and the narrow space in the CASK and the curved vacuum vessel(VV), it is difficult to perform online collision-free end effector trajectory tracking on EAMA. Current solution is to record a feasible trajectory offline by the operator after thousands of trials and errors as the reference for EAMA to track, which is time-consuming, inefficient, not friendly to operators, absence of resistance to external disturbances and difficult to be combined with other maintenance tasks like visual servoing. This paper proposes a Nonlinear Model Predictive Control (NMPC) based online trajectory tracking method, which is easy to use and flexible. The trajectory tracking problem is elegantly formulated as an optimization problem to minimize the end effector's tracking error while satisfying nonlinear constraints consisting of boundaries of state, output and control input, kinematic model of EAMA and collision avoidance constraints. Besides, a two-step recursive solver is developed to speed up the solving of NMPC problem to guarantee real-time control. The effectiveness and good performance of this method are demonstrated by an inspection simulation where the end effector tracks a semicircular trajectory.

中文翻译:

基于非线性模型预测控制 (NMPC) 的 EAST 铰接式维护臂 (EAMA) 轨迹跟踪

摘要 EAST 关节式维护臂 (EAMA) 是一种 8 自由度冗余关节式串行机械手,用于在 EAST(实验性先进超导托卡马克)中执行维护任务。由于机械手的冗余和总长度(8.7395 m)以及CASK和弯曲真空容器(VV)中的狭窄空间,难以在EAMA上进行在线无碰撞末端执行器轨迹跟踪。目前的解决方案是由运营商在经过数千次试错后离线记录一条可行的轨迹作为EAMA跟踪的参考,耗时、低效、对运营商不友好、缺乏抵抗外界干扰的能力且难以组合与其他维护任务,如视觉伺服。本文提出了一种基于非线性模型预测控制(NMPC)的在线轨迹跟踪方法,该方法使用方便、灵活。轨迹跟踪问题被优雅地表述为一个优化问题,以最小化末端执行器的跟踪误差,同时满足由状态边界、输出和控制输入、EAMA 运动学模型和避免碰撞约束组成的非线性约束。此外,开发了一个两步递归求解器来加速NMPC问题的求解,以保证实时控制。该方法的有效性和良好性能通过末端执行器跟踪半圆形轨迹的检查模拟得到证明。轨迹跟踪问题被优雅地表述为一个优化问题,以最小化末端执行器的跟踪误差,同时满足由状态边界、输出和控制输入、EAMA 运动学模型和避免碰撞约束组成的非线性约束。此外,开发了一个两步递归求解器来加速NMPC问题的求解,以保证实时控制。该方法的有效性和良好性能通过末端执行器跟踪半圆形轨迹的检查模拟得到证明。轨迹跟踪问题被优雅地表述为一个优化问题,以最小化末端执行器的跟踪误差,同时满足由状态边界、输出和控制输入、EAMA 运动学模型和避免碰撞约束组成的非线性约束。此外,开发了一个两步递归求解器来加速NMPC问题的求解,以保证实时控制。该方法的有效性和良好性能通过末端执行器跟踪半圆形轨迹的检查模拟得到证明。开发了一个两步递归求解器来加速NMPC问题的求解,以保证实时控制。该方法的有效性和良好性能通过末端执行器跟踪半圆形轨迹的检查模拟得到证明。开发了一个两步递归求解器来加速NMPC问题的求解,以保证实时控制。该方法的有效性和良好性能通过末端执行器跟踪半圆形轨迹的检查模拟得到证明。
更新日期:2021-02-01
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