Designing control inputs for a system that involves dynamical responses in multiple timescales is nontrivial. This paper proposes a parameterized time-warping function to enable a non-uniformly sampling along a prediction horizon given some parameters. The horizon should capture the responses under faster dynamics in the near future and preview the impact from slower dynamics in the distant future. Then a variable sampling MPC (VS-MPC) strategy is proposed to jointly determine optimal control and sampling parameters at each time instant. VS-MPC adapts how it samples along the horizon and determines optimal control accordingly at each time instant without any manual tuning or trial and error. A numerical example of a wind farm battery energy storage system is also provided to demonstrate that VS-MPC outperforms the uniform sampling MPC.

中文翻译：

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通过可微时间规整函数的可变采样 MPC

为涉及多个时间尺度的动态响应的系统设计控制输入并非易事。本文提出了一种参数化时间扭曲函数，可以在给定某些参数的情况下沿预测范围进行非均匀采样。地平线应该在不久的将来捕获更快动态下的响应，并预览遥远未来更慢动态的影响。然后提出了一种可变采样MPC（VS-MPC）策略来联合确定每个时刻的最优控制和采样参数。VS-MPC 调整它沿地平线的采样方式，并在每个时刻相应地确定最佳控制，而无需任何手动调整或反复试验。还提供了风电场电池储能系统的数值示例，以证明 VS-MPC 优于均匀采样 MPC。