提出一种更直观可调的机器人控制方法，兼顾性能与易用性。
From Instantaneous to Predictive Control: A More Intuitive and Tunable MPC Formulation for Robot Manipulators
- 保留瞬时控制的易调参数，通过预测时域提升性能。
- 在表面跟踪任务中验证，显著改善控制效果。
- 适合希望简化调参又追求高性能的工业应用开发者。
模型预测控制（MPC）因其相比瞬时控制方法的性能优势，日益成为机器人机械臂控制的热门选择。然而，控制器的调参仍是一大挑战。为此，我们提出一种实用的MPC公式，既保留了瞬时控制方法中更易理解的调参方式，又通过引入预测时域提升了性能。该方法基于一个简单示例展开，揭示了传统MPC调参的现实困难，并展示所提方法如何有效缓解这些问题。此外，该公式在表面跟踪任务上进行了验证，证明其在工业相关场景中的适用性。尽管研究聚焦于机器人机械臂控制，但预计该公式具有更广泛的应用潜力。
原文摘要 · Abstract (English)
Model predictive control (MPC) has become increasingly popular for the control of robot manipulators due to its improved performance compared to instantaneous control approaches. However, tuning these controllers remains a considerable hurdle. To address this hurdle, we propose a practical MPC formulation which retains the more interpretable tuning parameters of the instantaneous control approach while enhancing the performance through a prediction horizon. The formulation is motivated at hand of a simple example, highlighting the practical tuning challenges associated with typical MPC approaches and showing how the proposed formulation alleviates these challenges. Furthermore, the formulation is validated on a surface-following task, illustrating its applicability to industrially relevant scenarios. Although the research is presented in the context of robot manipulator control, we anticipate that the formulation is more broadly applicable.
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