This article is concerned with the tracking of nonequilibrium motions with model predictive control (MPC). It proposes to parametrize input and state trajectories of a dynamic system with basis functions to alleviate the computational burden in MPC. As a result of the parametrization, an optimization problem with fewer variables is obtained, and the memory requirements for storing the reference trajectories are reduced. The article also discusses the generation of feasible reference trajectories that account for the system's dynamics, as well as input and state constraints. In order to cope with repeatable disturbances, which may stem from unmodeled dynamics for example, an iterative learning procedure is included. The approach relies on a Kalman filter that identifies the repeatable disturbances based on previous trials. These are then included in the system's model available to the model predictive controller, which compensates them in subsequent trials. The proposed approach is evaluated on a quadcopter, whose task is to balance a pole, while flying a predefined trajectory.

中文翻译：

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用于轨迹跟踪的基于学习的参数化模型预测控制

本文涉及使用模型预测控制（MPC）跟踪非平衡运动。它建议对具有基本功能的动态系统的输入和状态轨迹进行参数化，以减轻MPC中的计算负担。作为参数化的结果，获得了具有较少变量的优化问题，并且减少了用于存储参考轨迹的存储器需求。本文还讨论了考虑系统动力学以及输入和状态约束的可行参考轨迹的生成。为了应对可能源自例如未建模动力学的可重复干扰，包括了迭代学习过程。该方法依赖于卡尔曼滤波器，该滤波器基于先前的试验识别可重复的干扰。然后将这些包含在模型预测控制器可用的系统模型中，以在后续试验中对其进行补偿。拟议的方法是在四轴飞行器上评估的，其任务是在飞行预定轨迹的同时平衡极点。