从实例数据集综合控制策略的概率方法
On a probabilistic approach to synthesize control policies from example datasets
摘要：
本文关注的是从示例数据集的控制策略的设计。所考虑的情况下，当只有一个黑盒描述的系统被控制是可用的，该系统是受致动约束。这些约束不一定由（可能是有噪声的）示例数据来满足，并且受控系统不一定与从其收集这些数据的系统相同。在这种情况下，我们引入了一些理论结果来计算示例数据集的控制策略：（i）使闭环系统的行为类似于数据中所示的行为;（ii）保证遵守约束。我们将控制问题转化为有限时间最优控制问题，并给出了其最优解的显式表达式。此外，我们将我们的发现转化为算法程序。该过程提供了一个系统的工具来计算的政策。我们的方法的有效性说明通过一个数值例子，在那里我们使用从测试驱动器收集到的真实数据，合成一个控制策略合并的汽车在高速公路上。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
