论文标题
分布强大的机会受限的启用数据的预测控制
Distributionally Robust Chance Constrained Data-enabled Predictive Control
论文作者
论文摘要
我们研究了未知随机线性时间不变系统的有限时间约束最佳控制的问题，这是预测控制算法的关键成分 - 尽管通常可以访问模型。我们提出了一种新颖的分布强大的支持数据的预测控制（DEEPC）算法，该算法使用噪声触发的输入/输出数据来预测未来的轨迹并计算最佳控制输入，同时满足输出机会限制。该算法基于（i）跨越系统行为的子空间的非参数表示，其中过去的轨迹在页面或汉克尔矩阵中排序； （ii）一种在分配上可靠的优化公式，可产生强大的概率性能保证。我们表明，对于某些目标函数，DEEDC表现出强大的样本外表现，同时尊重概率很高的约束。该算法为未知随机线性时变系统提供了控制设计的端到端方法。我们说明了在空中机器人案例研究中DEEDC的闭环性能。
We study the problem of finite-time constrained optimal control of unknown stochastic linear time-invariant systems, which is the key ingredient of a predictive control algorithm -- albeit typically having access to a model. We propose a novel distributionally robust data-enabled predictive control (DeePC) algorithm which uses noise-corrupted input/output data to predict future trajectories and compute optimal control inputs while satisfying output chance constraints. The algorithm is based on (i) a non-parametric representation of the subspace spanning the system behaviour, where past trajectories are sorted in Page or Hankel matrices; and (ii) a distributionally robust optimization formulation which gives rise to strong probabilistic performance guarantees. We show that for certain objective functions, DeePC exhibits strong out-of-sample performance, and at the same time respects constraints with high probability. The algorithm provides an end-to-end approach to control design for unknown stochastic linear time-invariant systems. We illustrate the closed-loop performance of the DeePC in an aerial robotics case study.
