论文标题
可扩展分布式控制的边界：SLS，MPC及以后
Frontiers in Scalable Distributed Control: SLS, MPC, and Beyond
论文作者
论文摘要
系统级合成（SLS）方法以易于理解的计算可扩展方式促进了大型网络物理网络的分布式控制。我们介绍了SLS方法及其在非线性控制，MPC，自适应控制和学习中的相关扩展的概述。为了说明基于SLS的方法的有效性，我们提出了一个案例研究，该案例研究是由功率电网促进的，具有通信约束，执行器饱和，干扰和变化的设定点。这个简单但具有挑战性的案例研究需要使用模型预测控制（MPC）。但是，标准MPC技术通常会缩放到大型系统上，并造成沉重的计算负担。为了应对这一挑战，我们将两个基于SLS的控制器结合在一起，形成一个分层的MPC状控制器。我们的控制器在系统大小方面具有恒定的计算复杂性，在在线计算要求中降低了20倍，并且仍然可以实现占集中式MPC控制器的3％以内的性能。
The System Level Synthesis (SLS) approach facilitates distributed control of large cyberphysical networks in an easy-to-understand, computationally scalable way. We present an overview of the SLS approach and its associated extensions in nonlinear control, MPC, adaptive control, and learning for control. To illustrate the effectiveness of SLS-based methods, we present a case study motivated by the power grid, with communication constraints, actuator saturation, disturbances, and changing setpoints. This simple but challenging case study necessitates the use of model predictive control (MPC); however, standard MPC techniques often scales poorly to large systems and incurs heavy computational burden. To address this challenge, we combine two SLS-based controllers to form a layered MPC-like controller. Our controller has constant computational complexity with respect to the system size, gives a 20-fold reduction in online computation requirements, and still achieves performance that is within 3% of the centralized MPC controller.
