Time-Varying Sensor and Actuator Selection for Uncertain Cyber-Physical Systems
This work provides a unified framework and practical algorithms for sensor and actuator selection in uncertain control systems, which is important for system designers but represents an incremental advance over existing optimization-based approaches.
The paper addresses time-varying sensor and actuator selection for uncertain cyber-physical systems by formulating the problem as mixed-integer bilinear matrix inequalities and proposing tractable methods to compute upper and lower bounds, along with a slicing algorithm for selection. Numerical experiments demonstrate the effectiveness of the proposed approaches compared to branch-and-bound and greedy methods.
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.