| 引用本文: | 平梦玲,刘斌,何芳,刘德旺,余梦琪.基于MESEN的机械臂运动预测控制[J].控制理论与应用,2025,42(12):2497~2507.[点击复制] |
| PING Meng-ling,LIU Bin,HE Fang,LIU De-wang,YU Meng-qi.Predictive control of robotic arm motion based on MESEN[J].Control Theory & Applications,2025,42(12):2497~2507.[点击复制] |
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| 基于MESEN的机械臂运动预测控制 |
| Predictive control of robotic arm motion based on MESEN |
| 摘要点击 105 全文点击 15 投稿时间：2024-07-12 修订日期：2025-05-18 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2025.40369 |
| 2025,42(12):2497-2507 |
| 中文关键词 机械臂运动控制 凸优化 模型预测控制 MESEN |
| 英文关键词 robotic arm motion control convex optimization model predictive control MESEN |
| 基金项目 国家自然科学基金项目(61963030),江西省教育厅科技项目(GJJ2401012)资助. |
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| 中文摘要 |
| 针对机械臂运动控制问题,首先建立与实体机械臂对应的DH参数表以及运动学方程,其次利用运动学设
计机械臂的位置预测控制器,然后针对控制器模型的凸优化问题,引入终端代价及终端约束,采用增强型迭代模型
预测控制策略来实现多轴机械臂运动控制.并采用误差求和增强型牛顿法来提升滚动优化过程中的迭代效率.此
外, 还提出了一种MESEN算法,通过有效控制算法步长以及模型中相关系数的大小,进一步提高系统的收敛速度,
减少优化过程对系统初始偏差的依赖.最后,利用Lyapunov定理对MESEN算法的收敛性进行分析,并对六轴机械臂
进行实验验证.实验结果表明,所提算法在大规模约束的模型中具有较好的效果. |
| 英文摘要 |
| Aimingatthemotioncontrol problem ofthemanipulator, first, the DH parameter table and kinematic equation
corresponding to the physical manipulator are established. Then, a position predictive controller of the manipulator is
designed using kinematics. Subsequently, to address the convex optimization problem of the controller model, the terminal
cost and terminal constraints are introduced, and the enhanced iterative model predictive control strategy is employed to
achieve the motion control of the multi-axis manipulator. The Error-Summing Enhanced Newton algorithm is used to
improve the iterative efficiency in the rolling optimization process. In addition, a MESEN algorithm is proposed which
further improves the convergence speed of the system and reduces the dependence of the optimization process on the
initial system deviation by effectively controlling the step size of the algorithm and the correlation coefficient in the model.
Finally, the convergence of the MESEN algorithm is analyzed using Lyapunov’s theorem, and the six-axis manipulator is
simulated and verified. Experimental results show that the proposed algorithm performs well in the large-scale constrained
model. |
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