引用本文:邢科新,倪伟琦,何德峰.荷载不确定移动机器人视觉伺服系统鲁棒预测控制[J].控制理论与应用,2022,39(2):327~335.[点击复制]
XING Ke-xin,NI Wei-qi,HE De-feng.Robust predictive control of visual servoing systems of mobile robots with load uncertainty[J].Control Theory and Technology,2022,39(2):327~335.[点击复制]
荷载不确定移动机器人视觉伺服系统鲁棒预测控制
Robust predictive control of visual servoing systems of mobile robots with load uncertainty
摘要点击 1955  全文点击 602  投稿时间：2020-12-22  修订日期：2021-12-07
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DOI编号  10.7641/CTA.2021.00915
  2022,39(2):327-335
中文关键词  移动机器人  视觉伺服  模型预测控制  约束  鲁棒稳定性
英文关键词  mobile robots  visual servoing  model predictive control  constraints  robust stability
基金项目  国家自然科学基金项目(61773345), 浙江省重点研发计划项目(2020C03056)资助.
作者单位E-mail
邢科新 浙江工业大学信息工程学院 xkx@zjut.edu.cn 
倪伟琦 浙江工业大学信息工程学院  
何德峰* 浙江工业大学信息工程学院 hdfzj@zjut.edu.cn 
中文摘要
      考虑具有可见性约束和执行器约束的载荷不确定移动机器人视觉伺服系统, 提出一种鲁棒视觉伺服预测 控制策略. 首先将该移动机器人视觉伺服系统建模为关于视觉伺服误差和驱动的不确定系统. 其次, 对约束的视觉 伺服误差子系统, 设计基于半正定规划的速度规划预测控制算法. 该算法分为离线计算和在线调度两个部分, 降低 预测控制算法的在线计算量. 而对约束的视觉伺服驱动子系统, 采用极小极大鲁棒预测控制算法, 实现对视觉伺服 误差子系统的规划速度的渐近跟踪. 进一步, 建立了载荷不确定移动机器人视觉伺服误差和驱动系统的鲁棒渐近稳 定性结果. 最后, 对比仿真结果验证了本文策略的有效性和优越性.
英文摘要
      The paper presents a robust visual servo predictive control scheme for visual servoing systems of mobile robots subject to visibility and actuator constraints and load uncertainty. Firstly, the visual servoing system of mobile robots is modelled as an uncertain system on visual servoing errors and driving. Secondly, the speed programming predictive control algorithm based on semi-definite programming is designed for the constrained visual servoing error subsystem. This algorithm is divided into two parts: offline computing and online scheduling, which reduces the online computational burden of the predictive control algorithm. The quasi-min-max robust predictive control algorithm is designed for the constrained visual servoing driving subsystem. The asymptotic tracking of the planning speed of the visual servo error subsystem is then realized. Moreover, the robust asymptotic stability result on the visual servoing errors and driving system of mobile robots with load uncertainty is established. Finally, some simulation results verify the effectiveness and superiority of the proposed strategy