针对物理耦合机器人路径精度问题，提出自适应模型预测控制方法。
Adaptive Model Predictive Control for Differential-Algebraic Systems towards a Higher Path Accuracy for Physically Coupled Robots
- 构建微分代数系统模型，在线估计不确定运动参数
- 仿真中路径跟踪误差降低88.6%（相比现有方法）
- 适合需要高精度协同的工业机器人场景
物理耦合的机器人在复杂制造过程中具有提升多机器人系统能力的潜力。然而，当前对物理耦合机器人路径跟踪精度的研究仍不充分，尤其未考虑运动参数不确定性、机械弹性及现成机器人的内置控制器影响。本文提出一种新型微分-代数系统模型，并通过真实执行数据验证。通过在线估计不确定运动参数以自适应模型，设计了一种作为机器人间协调器的自适应模型预测控制器。仿真结果显示，该控制器相较于当前最优基准，路径跟踪误差降低了88.6%。
原文摘要 · Abstract (English)
The physical coupling between robots has the potential to improve the capabilities of multi-robot systems in challenging manufacturing processes. However, the path tracking accuracy of physically coupled robots is not studied adequately, especially considering the uncertain kinematic parameters, the mechanical elasticity, and the built-in controllers of off-the-shelf robots. This paper addresses these issues with a novel differential-algebraic system model which is verified against measurement data from real execution. The uncertain kinematic parameters are estimated online to adapt the model. Consequently, an adaptive model predictive controller is designed as a coordinator between the robots. The controller achieves a path tracking error reduction of 88.6% compared to the state-of-the-art benchmark in the simulation.
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