Self-Tuning Network Control Architectures

TL;DR

This paper introduces a comprehensive framework for designing self-tuning network control architectures that adapt sensor and actuator placement and control policies to optimize performance in dynamic networks.

Contribution

It proposes a general mathematical formulation and solution structure for self-tuning network control, including a greedy heuristic for large networks and demonstrating significant performance improvements.

Findings

Self-tuning architectures outperform fixed architectures in numerical experiments.

Optimal policies in the linear quadratic case are piecewise quadratic and linear.

A greedy heuristic effectively manages large network design problems.

Abstract

We formulate a general mathematical framework for self-tuning network control architecture design. This problem involves jointly adapting the locations of active sensors and actuators in the network and the feedback control policy to all available information about the time-varying network state and dynamics to optimize a performance criterion. We propose a general solution structure analogous to the classical self-tuning regulator from adaptive control. We show that a special case with full-state feedback can be solved in principle with dynamic programming, and in the linear quadratic setting the optimal cost functions and policies are piecewise quadratic and piecewise linear, respectively. For large networks where exhaustive architecture search is prohibitive, we describe a greedy heuristic for joint architecture-policy design. We demonstrate in numerical experiments that self-tuning architectures can provide dramatically improved performance over fixed architectures. Our general formulation provides an extremely rich and challenging problem space with opportunities to apply a wide variety of approximation methods from stochastic control, system identification, reinforcement learning, and static architecture design.