论文标题
从示例数据集合成控制策略的概率方法中
On a probabilistic approach to synthesize control policies from example datasets
论文作者
论文摘要
本文涉及示例数据集的控制策略的设计。考虑的情况是，只有对要控制的系统的黑匣子描述，并且系统受到驱动约束的影响。这些限制不一定通过（可能是嘈杂的）示例数据来实现，并且所控制的系统不一定与收集这些数据的系统相同。在这种情况下，我们介绍了许多理论结果，以从示例数据集中计算一个控制策略：（i）使闭环系统的行为类似于数据中的说明； （ii）保证遵守约束。我们将控制问题作为有限的最佳控制问题重新提出，并为其最佳解决方案提供明确的表达。此外，我们将发现变成算法过程。该过程提供了一个系统的工具来计算策略。通过数值示例说明了我们方法的有效性，在该示例中，我们使用从测试驱动器收集的真实数据来合成控制策略以在高速公路上合并汽车。
This paper is concerned with the design of control policies from example datasets. The case considered is when just a black box description of the system to be controlled is available and the system is affected by actuation constraints. These constraints are not necessarily fulfilled by the (possibly, noisy) example data and the system under control is not necessarily the same as the one from which these data are collected. In this context, we introduce a number of theoretical results to compute a control policy from example datasets that: (i) makes the behavior of the closed-loop system similar to the one illustrated in the data; (ii) guarantees compliance with the constraints. We recast the control problem as a finite-horizon optimal control problem and give an explicit expression for its optimal solution. Moreover, we turn our findings into an algorithmic procedure. The procedure gives a systematic tool to compute the policy. The effectiveness of our approach is illustrated via a numerical example, where we use real data collected from test drives to synthesize a control policy for the merging of a car on a highway.
