Self-Tuning Network Control Architectures

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.

arXiv comment6 pages, 5 figures

References in corpus (5)

• On Submodularity and Controllability in Complex Dynamical Networks
• ${\mathcal{H}}_2$ performance of series-parallel networks: A compositional perspective
• Performance and design of consensus on matrix-weighted and time scaled graphs
• Online Actuator Selection and Controller Design for Linear Quadratic Regulation with Unknown System Model
• Self-Tuning Network Control Architectures

Cited by in corpus (1)

• Self-Tuning Network Control Architectures