Differential balancing theory for nonlinear model reduction relies on differential controllability and observability functions. In this paper, we further investigate them from two different perspectives. First, we establish novel connections between these differential energy functions and their incremental counterparts by assuming the existence of the corresponding optimal state feedback for each controllability function. Specifically, an upper bound on the incremental controllability/observability function is provided by the corresponding differential energy function. Conversely, an upper bound on the differential controllability function can be estimated from the incremental controllability function. Furthermore, the differential observability function can be constructed from the incremental observability function. Second, we explore the positive definiteness of the differential controllability/observability function in the context of controllability/observability and stability.
Control-Theoretic View of Neural ODEs: Empirical Controllability and Observability
The University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校）；AI, Autonomy, Resilience, Control (AARC) Lab at the Department of Electrical and Computer Engineering, The University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校电气与计算机工程系自主、韧性控制实验室）
Nonlinear Model Reduction in Power Systems by Balancing of Empirical Controllability and Observability Covariances
Energy Systems Division, Argonne National Laboratory（阿贡国家实验室能源系统部）；Department of Electrical Engineering, Guangxi University（广西大学电气工程学院）；Energy and Transportation Sciences Division, Oak Ridge National Laboratory（橡树岭国家实验室能源与交通科学部）
Structural accessibility and structural observability of nonlinear networked systems
CONACyT - Institute of Mathematics, Universidad Nacional Autonoma de Mexico（墨西哥国立自治大学数学研究所）；Institute of Mathematics, Universidad Nacional Autonoma de Mexico（墨西哥国立自治大学数学研究所）；Laboratoire des Sciences du Numérique de Nantes, UMR CNRS 6004（南特数字科学实验室）
