论文标题
对线性二次调节器的强大结构控制约束施加强大的结构化控制约束
Imposing Robust Structured Control Constraint on Reinforcement Learning of Linear Quadratic Regulator
论文作者
论文摘要
本文讨论了学习结构化反馈控制，以获得具有未知状态矩阵的线性动态系统的外源输入的足够鲁棒性。对于许多网络物理系统来说，对控制器的结构约束是必要的，我们的方法为任何通用结构提供了设计，为分布式学习控制铺平了道路。加强学习（RL）以及控制理论足够的稳定性和性能保证的想法用于开发方法。首先，使用动态编程制定了基于模型的框架，以将结构约束嵌入线性二次调节器（LQR）设置中，以及足够的鲁棒性条件。此后，我们将这些条件转化为基于数据驱动的学习框架 - 强大的结构增强学习（RSRL），该框架享有稳定性和融合的控制理论保证。我们通过在具有6个代理的多代理网络上进行模拟来验证我们的理论结果。
This paper discusses learning a structured feedback control to obtain sufficient robustness to exogenous inputs for linear dynamic systems with unknown state matrix. The structural constraint on the controller is necessary for many cyber-physical systems, and our approach presents a design for any generic structure, paving the way for distributed learning control. The ideas from reinforcement learning (RL) in conjunction with control-theoretic sufficient stability and performance guarantees are used to develop the methodology. First, a model-based framework is formulated using dynamic programming to embed the structural constraint in the linear quadratic regulator (LQR) setting along with sufficient robustness conditions. Thereafter, we translate these conditions to a data-driven learning-based framework - robust structured reinforcement learning (RSRL) that enjoys the control-theoretic guarantees on stability and convergence. We validate our theoretical results with a simulation on a multi-agent network with 6 agents.
