Communication Delay Co-Design in $\mathcal{H}_2$ Distributed Control Using Atomic Norm Minimization
For control theorists and engineers, this work extends the RFD framework to communication architecture design, which was previously missing, but the contribution is incremental as it applies existing atomic norm techniques to a new domain.
This paper introduces an atomic norm for designing communication architectures in distributed control, enabling the co-design of communication architecture and control law via convex optimization. The resulting problem is solved using finite-dimensional second-order cone programming.
When designing distributed controllers for large-scale systems, the actuation, sensing and communication architectures of the controller can no longer be taken as given. In particular, controllers implemented using dense architectures typically outperform controllers implemented using simpler ones -- however, it is also desirable to minimize the cost of building the architecture used to implement a controller. The recently introduced Regularization for Design (RFD) framework poses the controller architecture/control law co-design problem as one of jointly optimizing the competing metrics of controller architecture cost and closed loop performance, and shows that this task can be accomplished by augmenting the variational solution to an optimal control problem with a suitable atomic norm penalty. Although explicit constructions for atomic norms useful for the design of actuation, sensing and joint actuation/sensing architectures are introduced, no such construction is given for atomic norms used to design communication architectures. This paper describes an atomic norm that can be used to design communication architectures for which the resulting distributed optimal controller is specified by the solution to a convex program. Using this atomic norm we then show that in the context of $\mathcal{H}_2$ distributed optimal control, the communication architecture/control law co-design task can be performed through the use of finite dimensional second order cone programming.