Data-Driven Optimal Control of Bilinear Systems
Data-Driven Optimal Control of Bilinear Systems
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DOI：
10.1109/lcsys.2022.3164983
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发表时间：
2021-12
影响因子：
3
通讯作者：
Zhenyi Yuan;J. Cortés
中科院分区：
文献类型：
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作者：
Zhenyi Yuan;J. Cortés
这封信开发了一种方法来学习最优控制的双线性系统的数据没有先验知识的动态。给定一个未知的双线性系统，我们的特征时，可用的数据是足够的信息来解决最优控制问题。这种特性使我们提出了一个在线控制实验设计过程，保证任何输入/状态轨迹可以表示为一个线性组合的输入/状态数据矩阵。利用这种表示，我们将原来的最优控制问题转化为一个等价的基于数据的双线性约束优化问题。我们解决后者迭代采用凹凸程序找到一个局部最优控制序列。仿真结果表明，所提出的基于数据的方法的性能与基于模型的方法。
This letter develops a method to learn optimal controls from data for bilinear systems without a priori knowledge of the dynamics. Given an unknown bilinear system, we characterize when the available data is sufficiently informative to solve the optimal control problem. This characterization leads us to propose an online control experiment design procedure that guarantees that any input/state trajectory can be represented as a linear combination of collected input/state data matrices. Leveraging this representation, we transform the original optimal control problem into an equivalent data-based optimization problem with bilinear constraints. We solve the latter by iteratively employing a convex-concave procedure to find a locally optimal control sequence. Simulations show that the performance of the proposed data-based approach is comparable with model-based methods.