李艳, 雷佳琦. 扰动作用下的多移动机器人编队模型预测控制[J]. 信息与控制, 2023, 52(2): 166-175. DOI: 10.13976/j.cnki.xk.2023.2181
引用本文: 李艳, 雷佳琦. 扰动作用下的多移动机器人编队模型预测控制[J]. 信息与控制, 2023, 52(2): 166-175. DOI: 10.13976/j.cnki.xk.2023.2181
LI Yan, LEI Jiaqi. Formation Model Predictive Control of Multi-mobile Robots under Disturbance[J]. INFORMATION AND CONTROL, 2023, 52(2): 166-175. DOI: 10.13976/j.cnki.xk.2023.2181
Citation: LI Yan, LEI Jiaqi. Formation Model Predictive Control of Multi-mobile Robots under Disturbance[J]. INFORMATION AND CONTROL, 2023, 52(2): 166-175. DOI: 10.13976/j.cnki.xk.2023.2181

扰动作用下的多移动机器人编队模型预测控制

Formation Model Predictive Control of Multi-mobile Robots under Disturbance

  • 摘要: 针对多移动机器人编队中的领航者和跟随者同时受系统内部未建模动态和风向、路面平整度等内外扰动影响而带来的无法保持编队队形的问题,提出了基于扩张状态观测器(ESO)的模型预测(MPC)编队控制方法。首先,建立带有扰动项的领航跟随编队模型,然后分别设计ESO对领航者和跟随者所受扰动进行估计,将该编队模型进行线性化离散化处理作为MPC编队控制器的预测模型,在预测输出方程中引入对扰动的估计结果,最后通过滚动优化求解最优控制律,实现对多移动机器人的鲁棒编队控制。仿真实验结果验证了所提出的控制方法的有效性。

     

    Abstract: In multi-mobile robot formation, the leader and follower cannot maintain formation because of the unmodeled dynamics inside the system and external disturbances such as wind direction and road surface smoothness. Thus, in this study, we propose a model prediction control (MPC) method based on an extended state observer (ESO). First, we establish a leader-follower formation model with disturbance term, and then design ESOs to estimate the disturbance of leader and follower. The formation model is linearized and discretized as the prediction model of the MPC formation controller, and the disturbance estimation is added to the prediction output equation. Finally, robust formation control for multi-mobile robots is achieved via optimal control law. The simulation results verify the effectiveness of the proposed control method.

     

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