Time-Varying Sensor and Actuator Selection for Uncertain Cyber-Physical Systems

TL;DR

This paper introduces tractable optimization methods for time-varying sensor and actuator selection in uncertain cyber-physical systems, addressing computational challenges through convex approximations, relaxations, and heuristics.

Contribution

It formulates SaA selection as MIBMI problems and develops novel bounding and slicing algorithms, including branch-and-bound and greedy methods, for effective solutions.

Findings

Proposed convex and relaxation techniques provide effective bounds.

Numerical experiments demonstrate the methods' effectiveness.

Heuristics outperform traditional approaches in complex scenarios.

Abstract

We propose methods to solve time-varying, sensor and actuator (SaA) selection problems for uncertain cyber-physical systems. We show that many SaA selection problems for optimizing a variety of control and estimation metrics can be posed as semidefinite optimization problems with mixed-integer bilinear matrix inequalities (MIBMIs). Although this class of optimization problems are computationally challenging, we present tractable approaches that directly tackle MIBMIs, providing both upper and lower bounds, and that lead to effective heuristics for SaA selection. The upper and lower bounds are obtained via successive convex approximations and semidefinite programming relaxations, respectively, and selections are obtained with a novel slicing algorithm from the solutions of the bounding problems. Custom branch-and-bound and combinatorial greedy approaches are also developed for a broad class of systems for comparison. Finally, comprehensive numerical experiments are performed to compare the different methods and illustrate their effectiveness.