用核插值高效安全地逼近模型预测控制器，提升系统性能。
Safe and High-Performance Learning of Model Predicitve Control using Kernel-Based Interpolation
- 基于核插值与数据评分机制，选择关键数据点降低计算复杂度。
- 仅在闭环可达状态集内保证精度，显著减少逼近所需数据量。
- 结合蒙特卡洛可达性分析，确保控制策略的安全性与高性能。
我们提出一种方法，通过核插值高效且安全地逼近模型预测控制器。由于近似函数的计算复杂度随数据点数量线性增长，我们引入评分函数以选取最具潜力的数据点。为进一步降低逼近复杂度，我们仅关注闭环可达状态集合，即近似函数只需在此集合内保持高精度。该方法特别适用于初始条件集合较小的系统。为保障设计的近似控制器具备安全性和高性能，我们采用基于蒙特卡洛方法的可达性分析。
原文摘要 · Abstract (English)
We present a method that allows efficient and safe approximation of model predictive controllers using kernel interpolation. Since the computational complexity of the approximating function scales linearly with the number of data points, we propose to use a scoring function which chooses the most promising data. To further reduce the complexity of the approximation, we restrict our considerations to the set of closed-loop reachable states. That is, the approximating function only has to be accurate within this set. This makes our method especially suited for systems, where the set of initial conditions is small. In order to guarantee safety and high performance of the designed approximated controller, we use reachability analysis based on Monte Carlo methods.
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