A Convex Information Relaxation for Constrained Decentralized Control Design Problems

TL;DR

This paper introduces a convex relaxation technique to compute lower bounds for complex constrained decentralized control problems with nonclassical information structures, aiding in understanding their optimal costs.

Contribution

It develops a convex programming approach that expands information structures to derive computable lower bounds on decentralized control costs.

Findings

Provides a convex relaxation framework for decentralized control problems.

Derives finite-dimensional conic programs as lower bounds.

Applicable to systems with polyhedral constraints and sparsity in information structures.

Abstract

We describe a convex programming approach to the calculation of lower bounds on the minimum cost of constrained decentralized control problems with nonclassical information structures. The class of problems we consider entail the decentralized output feedback control of a linear time-varying system over a finite horizon, subject to polyhedral constraints on the state and input trajectories, and sparsity constraints on the controller's information structure. As the determination of optimal control policies for such systems is known to be computationally intractable in general, considerable effort has been made in the literature to identify efficiently computable, albeit suboptimal, feasible control policies. The construction of computationally tractable bounds on their suboptimality is the primary motivation for the techniques developed in this note. Specifically, given a decentralized control problem with nonclassical information, we characterize an expansion of the given information structure, which ensures its partial nestedness, while maximizing the optimal value of the resulting decentralized control problem under the expanded information structure. The resulting decentralized control problem is cast as an infinite-dimensional convex program, which is further relaxed via a partial dualization and restriction to affine dual control policies. The resulting problem is a finite-dimensional conic program whose optimal value is a provable lower bound on the minimum cost of the original constrained decentralized control problem.