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.

翻译：模型预测控制（MPC）因其性能优于瞬时控制方法，在机器人操作臂控制中日益普及。然而，这类控制器的参数整定仍是一个重大挑战。为应对这一挑战，我们提出了一种实用的MPC框架，该框架保留了瞬时控制方法中更具可解释性的调参参数，同时通过预测时域提升了控制性能。我们通过一个简单示例阐述该框架的设计动机，揭示典型MPC方法在实际调参中面临的挑战，并展示所提框架如何缓解这些挑战。此外，通过在曲面跟踪任务中的验证，说明了该框架在工业相关场景中的适用性。虽然本研究以机器人操作臂控制为背景展开，但我们预期该框架具有更广泛的适用性。