Abstract:
Optimization of controllability, i.e., to optimize the degree of the controllability using the flexibility of a system such as choosing input nodes and designing network ...Show MoreMetadata
Abstract:
Optimization of controllability, i.e., to optimize the degree of the controllability using the flexibility of a system such as choosing input nodes and designing network structure, is growing in importance to network systems. For example, if a network system has strong controllability for a certain control input, we know that the input is effective for controlling the system even under a limited energy condition. If a mathematical model of the system is available, the controllability can be optimized by a model-based method. However, we often face the difficulty to incorporate useful prior knowledge of the system. In such a case, a data-driven approach is promising. In this paper, as the initial step toward this direction, we address the problem of determining the sensitivity of a controllability measure with respect to an edge weight of a network system in a data-driven manner. By characterizing the sensitivity by two Lyapunov equations, we clarify that the sensitivity is derived from the so-called data-driven Lyapunov equations. Moreover, this result is extended to the case of high-order sensitivity.
Published in: 2022 IEEE 61st Conference on Decision and Control (CDC)
Date of Conference: 06-09 December 2022
Date Added to IEEE Xplore: 10 January 2023
ISBN Information: