深层结构团队中的滚动时域控制：一种可证明易处理的大规模方法及其在群机器人中的应用
Receding Horizon Control in Deep Structured Teams: A Provably Tractable Large-Scale Approach with Application to Swarm Robotics
摘要：
在本文中，一个深结构化的跟踪问题，大量的决策者。该问题被表述为一个线性二次深度结构团队，其中决策者希望在考虑局部目标的同时合作跟踪全局目标。对于无约束的设置，规范变换技术被用来分解所产生的优化问题，以获得一个低维的最优控制策略的局部和全局Riccati方程。然而，对于约束的情况下，可行集不一定是可分解的规范变换。为了克服这一障碍，我们提出了一个家庭的本地和全球滚动时域控制问题，其中精心构造的线性组合的解决方案提供了一个可行的解决方案，为原来的约束问题。上述解决方案的突出特性是，它们相对于决策者的数量是易处理的，并且可以以分布式方式实现。此外，主要结果被推广到多个子种群和多特征的情况下，包括领导者-追随者设置，内聚代价函数和软结构约束。此外，根据扰动影响因素提出了一类网络物理攻击。一个数值例子来证明结果的有效性。
英文摘要：
In this paper, a deep structured tracking problem is introduced for a large number of decision-makers. The problem is formulated as a linear quadratic deep structured team, where the decision-makers wish to track a global target cooperatively while considering their local targets. For the unconstrained setup, the gauge transformation technique is used to decompose the resultant optimization problem in order to obtain a low-dimensional optimal control strategy in terms of the local and global Riccati equations. For the constrained case, however, the feasible set is not necessarily decomposable by the gauge transformation. To overcome this hurdle, we propose a family of local and global receding horizon control problems, where a carefully constructed linear combination of their solutions provides a feasible solution for the original constrained problem. The salient property of the above solutions is that they are tractable with respect to the number of decision-makers and can be implemented in a distributed manner. In addition, the main results are generalized to cases with multiple sub-populations and multiple features, including leader-follower setup, cohesive cost function and soft structural constraint. Furthermore, a class of cyber-physical attacks is proposed in terms of perturbed influence factors. A numerical example is presented to demonstrate the efficacy of the results.
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。
