Hierarchical H2 Control of Large-Scale Network Dynamic Systems
For control engineers designing scalable controllers for large-scale networked systems, this work provides a method to achieve near-optimal performance with reduced communication and computational demands.
The paper addresses scalability issues in H2 optimal control for large-scale networked systems by introducing a class of structural constraints that yield a convex reformulation. The resulting hierarchical controller reduces communication links and computational complexity, demonstrated on a 500-node network.
Standard H2 optimal control of networked dynamic systems tend to become unscalable with network size. Structural constraints can be imposed on the design to counteract this problem albeit at the risk of making the solution non-convex. In this paper, we present a special class of structural constraints such that the H2 design satisfies a quadratic invariance condition, and therefore can be reformulated as a convex problem. This special class consists of structured and weighted projections of the input and output spaces. The choice of these projections can be optimized to match the closed-loop performance of the reformulated controller with that of the standard H2 controller. The advantage is that unlike the latter, the reformulated controller results in a hierarchical implementation which requires significantly lesser number of communication links, while also admitting model and controller reduction that helps the design to scale computationally. We illustrate our design with simulations of a 500-node network.