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

We propose methods to solve time-varying, sensor and actuator (SaA) selection problems for uncertain cyberphysical 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 is computationally challenging, we present tractable approaches that directly tackle MIBMIs while providing both upper and lower bounds and 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.